Transmodal input fusion for a wearable system

ABSTRACT

Examples of wearable systems and methods can use multiple inputs (e.g., gesture, head pose, eye gaze, voice, totem, and/or environmental factors (e.g., location)) to determine a command that should be executed and objects in the three-dimensional (3D) environment that should be operated on. The wearable system can detect when different inputs converge together, such as when a user seeks to select a virtual object using multiple inputs such as eye gaze, head pose, hand gesture, and totem input. Upon detecting an input convergence, the wearable system can perform a transmodal filtering scheme that leverages the converged inputs to assist in properly interpreting what command the user is providing or what object the user is targeting.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 16/418,820, filed May 21, 2019, which is entitled “TransmodalInput Fusion for a Wearable System,” which claims the benefit ofpriority to U.S. Provisional Patent Application No. 62/675,164, filed onMay 22, 2018, which is entitled “Electromyographic Sensor Prediction inAugmented Reality,” and to U.S. Provisional Patent Application No.62/692,519, filed on Jun. 29, 2018, which is entitled “Transmodal InputFusion for a Wearable System;” the disclosures of which are herebyincorporated by reference herein in their entireties.

COPYRIGHT STATEMENT

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The present disclosure relates to virtual reality and augmented realityimaging and visualization systems and more particularly to dynamicallyfusing multiple modes of user input to facilitate interacting withvirtual objects in a three-dimensional (3D) environment.

BACKGROUND

Modem computing and display technologies have facilitated thedevelopment of systems for so called “virtual reality”, “augmentedreality”, or “mixed reality” experiences, wherein digitally reproducedimages or portions thereof are presented to a user in a manner whereinthey seem to be, or may be perceived as, real. A virtual reality, or“VR”, scenario typically involves presentation of digital or virtualimage information without transparency to other actual real-world visualinput; an augmented reality, or “AR”, scenario typically involvespresentation of digital or virtual image information as an augmentationto visualization of the actual world around the user; a mixed reality,or “MR”, related to merging real and virtual worlds to produce newenvironments where physical and virtual objects co-exist and interact inreal time. As it turns out, the human visual perception system is verycomplex, and producing a VR, AR, or MR technology that facilitates acomfortable, natural-feeling, rich presentation of virtual imageelements amongst other virtual or real-world imagery elements ischallenging. Systems and methods disclosed herein address variouschallenges related to VR, AR and MR technology.

SUMMARY

Examples of wearable systems and methods described herein can usemultiple inputs (e.g., gesture, head pose, eye gaze, voice, from userinput devices, or environmental factors (e.g., location)) to determine acommand that should be executed or objects in the three dimensional (3D)environment that should be operated on or selected. The multiple inputscan also be used by the wearable device to permit a user to interactwith physical objects, virtual objects, text, graphics, icons, userinterfaces, and so forth.

For example, a wearable display device can be configured to dynamicallyparse multiple sensor inputs for execution of a task or targeting anobject. The wearable device can dynamically use a combination ofmultiple inputs such as head pose, eye gaze, hand, arm, or bodygestures, voice commands, user input devices, environmental factors(e.g., the user's location or the objects around the users) to determinewhich object in the user's environment the user intends to select oractions the wearable device may perform. The wearable device candynamically choose a set of sensor inputs that together indicate theuser's intent to select a target object (inputs that provide independentor supplemental indications of the user's intent to select a targetobject may called convergent or converging inputs). The wearable devicecan combine or fuse the inputs from this set (e.g., to enhance thequality of the user interaction as described herein). If a sensor inputfrom this set later shows divergence from the target object, thewearable device can cease using (or reduce the relative weight given to)the divergent sensor input.

The process of dynamically using converged sensor inputs while ignoring(or reducing the relative weight given to) diverged sensor inputs issometimes referred to herein as transmodal input fusion (or simplytransmodal fusion) and can provide substantial advantages overtechniques that simply accept inputs from multiple sensors. Transmodalinput fusion can anticipate or even predict, on a dynamic, real-timebasis, which of the many possible sensor inputs are the appropriatemodal inputs that convey the user's intent to target or operate on areal or virtual object in the user's 3D AR/MR/VR environment.

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Neitherthis summary nor the following detailed description purports to defineor limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson.

FIGS. 2A and 2B schematically illustrate examples of a wearable systemthat can be configured to use the transmodal input fusion techniquesdescribed herein.

FIG. 3 schematically illustrates aspects of an approach for simulatingthree-dimensional imagery using multiple depth planes.

FIG. 4 schematically illustrates an example of a waveguide stack foroutputting image information to a user.

FIG. 5 shows example exit beams that may be outputted by a waveguide.

FIG. 6 is a schematic diagram showing an optical system including awaveguide apparatus, an optical coupler subsystem to optically couplelight to or from the waveguide apparatus, and a control subsystem, usedin the generation of a multi-focal volumetric display, image, or lightfield.

FIG. 7 is a block diagram of an example of a wearable system.

FIG. 8 is a process flow diagram of an example of a method of renderingvirtual content in relation to recognized objects.

FIG. 9 is a block diagram of another example of a wearable system.

FIG. 10 is a process flow diagram of an example of a method fordetermining user input to a wearable system.

FIG. 11 is a process flow diagram of an example of a method forinteracting with a virtual user interface.

FIG. 12A schematically illustrates an example of a field of regard(FOR), a field of view (FOV) of a world camera, a field of view of auser, and a field of fixation of a user.

FIG. 12B schematically illustrates an example of virtual objects in auser's field of view and virtual objects in a field of regard.

FIG. 13 illustrates examples of interacting with a virtual object usingone mode of user input.

FIG. 14 illustrates examples of selecting a virtual object using acombination of user input modes.

FIG. 15 illustrates an example of interacting with a virtual objectusing a combination of direct user inputs.

FIG. 16 illustrates an example computing environment for aggregatinginput modes.

FIG. 17A illustrates an example of identifying a target virtual objectusing a lattice tree analysis.

FIG. 17B illustrates an example of determining a target user interfaceoperation based on multimodal inputs.

FIG. 17C illustrates an example of aggregating confidence scoresassociated with input modes for a virtual object.

FIGS. 18A and 18B illustrate examples of calculating confidence scoresfor objects within a user's FOV.

FIGS. 19A and 19B illustrate an example of interacting with a physicalenvironment using multimodal inputs.

FIG. 20 illustrates an example of automatically resizing a virtualobject based on multimodal inputs.

FIG. 21 illustrates an example of identifying a target virtual objectbased on objects' locations.

FIGS. 22A and 22B illustrate another example of interacting with auser's environment based on a combination of direct and indirect inputs.

FIG. 23 illustrates an example process of interacting with a virtualobject using multimodal inputs.

FIG. 24 illustrates an example of setting direct input modes associatedwith a user interaction.

FIG. 25 illustrates an example of user experience with multimodal input.

FIG. 26 illustrates an example user interface with a variety ofbookmarked applications.

FIG. 27 illustrates an example user interface when a search command isissued.

FIGS. 28A-28F illustrate an example user experience of composing andediting a text based on a combination of voice and gaze inputs.

FIG. 29 illustrates an example of selecting a word based on an inputfrom a user input device and gaze.

FIG. 30 illustrates an example of selecting a word for editing based ona combination of voice and gaze inputs.

FIG. 31 illustrates an example of selecting a word for editing based ona combination of gaze and gesture inputs.

FIG. 32 illustrates an example of replacing a word based on acombination of eye gaze and voice inputs.

FIG. 33 illustrates an example of changing a word based on a combinationof voice and gaze inputs.

FIG. 34 illustrates an example of editing a selected word using avirtual keyboard.

FIG. 35 illustrates an example user interface that displays possibleactions to apply to a selected word.

FIG. 36 illustrates examples of interacting with a phrase usingmultimodal inputs.

FIGS. 37A and 37B illustrate additional examples of using multimodalinputs to interact with a text.

FIG. 38 is a process flow diagram of an example method of using multiplemodes of user input to interact with a text.

FIG. 39A illustrates examples of user inputs received through controllerbuttons.

FIG. 39B illustrates examples of user inputs received through acontroller touchpad.

FIG. 39C illustrates examples of user inputs received through physicalmovement of a controller or a head-mounted device (HMD).

FIG. 39D illustrates examples of how user inputs may have differentdurations.

FIG. 40A illustrates additional examples of user inputs received throughcontroller buttons.

FIG. 40B illustrates additional examples of user inputs received througha controller touchpad.

FIG. 41A illustrates examples of user inputs, received through variousmodes of user input, for spatial manipulation of a virtual environmentor virtual objects.

FIG. 41B illustrates examples of user inputs, received through variousmodes of user input, for interacting with planar objects.

FIG. 41C illustrates examples of user inputs, received through variousmodes of user input, for interacting with a wearable system.

FIGS. 42A, 42B, and 42C illustrate examples of user inputs in the formof fine finger gestures and hand movements.

FIG. 43A illustrates examples of fields of perception of a user of awearable system including a visual perception field and an auditoryperception field.

FIG. 43B illustrates examples of display render planes of a wearablesystem having multiple depth planes.

FIGS. 44A, 44B, and 44C illustrate examples of different interactiveregions, whereby a wearable system may receive and respond to userinputs differently depending on which interactive region the user isinteractive with.

FIG. 45 illustrates an example of a unimodal user interaction.

FIGS. 46A, 46B, 46C, 46D, and 46E illustrate examples of multimodal userinteractions.

FIGS. 47A, 47B, and 47C illustrate examples of crossmodal userinteractions.

FIGS. 48A, 48B, and 49 illustrate examples of transmodal userinteractions.

FIG. 50 is a process flow diagram of an example of a method of detectingmodal vergences.

FIG. 51 illustrates examples of user selections in unimodal, bi-modal,and tri-modal interactions.

FIG. 52 illustrates an example of interpreting user input based on theconvergence of multiple modes of user input.

FIG. 53 illustrates an example of how different user inputs may convergeacross different interactive regions.

FIGS. 54, 55, and 56 illustrate examples of how a system may selectamongst multiple possible input convergence interpretations based atleast in part on a ranking of different inputs.

FIGS. 57A and 57B are block diagrams of an example of a wearable systemthat fuses multiple modes of user input to facilitate user interactionswith the wearable system.

FIG. 58A is a graph of vergence distances of various input pairs and ofthe vergence area for a user interaction with dynamic transmodal inputfusion disabled.

FIG. 58B is a graph of vergence distances of various input pairs and ofthe vergence area for a user interaction with dynamic transmodal inputfusion enabled.

FIGS. 59A and 59B illustrate an example of user interaction and feedbackduring a fixation and dwell event.

FIGS. 60A and 60B illustrate examples of a wearable system that includesat least one neuromuscular sensor such as, e.g., an electromyogram (EMG)sensor, and that can be configured to use embodiments of the transmodalinput fusion techniques described herein.

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

DETAILED DESCRIPTION Overview

Modern computing systems can possess a variety of user interactions. Awearable device can present an interactive VR/AR/MR environment whichcan comprise data elements that may be interacted with by a user througha variety of inputs. Modern computing systems are typically engineeredto generate a given output based on a single direct input. For example,a keyboard will relay text input as received from finger strokes of auser. A voice recognition application can create an executable datastring based on a user's voice as a direct input. A computer mouse canguide a cursor in response to a user's direct manipulation (e.g., theuser's hand movement or gesture). The various ways a user can interactwith the system are sometimes referred to herein as modes of userinputs. For example, a user input via a mouse or keyboard is ahand-gesture-based mode of interaction (because the fingers of a handpress keys on a keyboard or the hand moves a mouse).

However, conventional input techniques, such as keyboard, user inputdevice, gestures, etc., in a data rich and dynamic interactionenvironment (e.g., the AR/VR/MR environment) may require a high degreeof specificity to accomplish a desired task. Otherwise, in the absenceof precise inputs, the computing systems may suffer a high error rateand may cause incorrect computer operations to be performed. Forexample, when a user intends a move an object in a 3D space using atouchpad, the computing systems may not be able to correctly interpret amove command if the user does not specify a destination or specify theobject using the touchpad. As another example, inputting a string oftext using a virtual keyboard (e.g., as manipulated with a user inputdevice or by gesture) as the only mode of input can be slow andphysically fatiguing, because it requires prolonged fine motor controlto type the described keys in mid-air or on a physical surface (e.g., adesk) where the virtual keyboard is rendered.

To reduce the degree of specificity required in an input command and toreduce error rate associated with an imprecise command, the wearablesystem described herein can be programmed to dynamically apply multipleinputs for identification of an object to be selected or acted upon,execution of an interaction event associated with the object, such ase.g., a task for selecting, moving, resizing, or targeting a virtualobject. The interaction event can include causing an application(sometimes referred to as an app) associated with the virtual object toexecute (e.g., if the target object is a media file, the interactionevent can comprise causing a media player to play the media file (e.g.,a song or video)). Selecting the target virtual object can compriseexecuting an application associated with the target virtual object. Asdescribed below, the wearable device can dynamically select which of twoor more types of input (or inputs from multiple input channels orsensors) to generate the command for execution of a task or to identifythe target object on which the command is to be executed.

The particular sensor inputs that are used at any point in time canchange dynamically as the user interacts with the 3D environment. Aninput mode can be dynamically added (or “fused” as further describedherein) when the device determines that the input mode is providingadditional information to aid in the targeting of a virtual object, andan input mode can be dynamically removed if that input mode no longerprovides relevant information. For example, the wearable device maydetermine that a user's head pose and eye gaze are directed at a targetobject. The device can use these two input modes to assist in selectionof the target object. If the device determines that the user is alsopointing a totem at the target object, the device can dynamically addthe totem input to the head pose and eye gaze inputs, which may providefurther certainty that the user intends to select the target object. Itcan be said that the totem input has “converged” with the head poseinput and the eye gaze input. Continuing with this example, if the userglances away from the target object so that the user's eye gaze is nolonger directed at the target object, the device may cease using eyegaze input while continuing to use totem input and head pose input. Inthis case, it can be said that the eye gaze input has “diverged” fromthe totem input and the head pose input.

The wearable device can dynamically determine divergence and convergenceevents occurring among the multiple input modes and can dynamicallyselect, from among these multiple input modes, a subset of input modesthat are relevant to the user's interaction with the 3D environment. Forexample, the system can use the input modes that have converged and candisregard input modes that have diverged. The number of input modes thatcan be dynamically fused or filtered in response to input convergence isnot limited to the three modes described in this example (totem, headpose, eye gaze), and can dynamically switch among 1, 2, 3, 4, 5, 6, ormore sensor inputs (as different input modes converge or diverge).

The wearable device can use convergent inputs by accepting the inputsfor analysis, by increasing computing resources available or assigned tothe convergent inputs (e.g., to the input sensor assemblies), byselecting a particular filter to apply to one or more of the convergentinputs, by taking other suitable action, and/or by any combination ofthese actions. The wearable device may not use, cease using, or reducethe weight given to sensor inputs that are divergent or diverging.

Input modes may be said to converge, for example, when a variancebetween input vectors of the inputs are less than a threshold. Once thesystem has recognized that inputs are converged, the system may filterthe converged inputs and fuse them together to create a new conditionedinput that can then be used to do useful work and be utilized to performa task with greater confidence and accuracy (than could be accomplishedby using the inputs separately). In various embodiments, the system mayapply dynamic filtering (e.g., dynamically fuse inputs together) inresponse to the relative convergence of inputs. The system maycontinually evaluate whether inputs are convergent. In some embodiments,the system may scale the intensity of input fusion (e.g., how stronglythe system fuses inputs together) in relation to the intensity of inputconvergence (e.g., how closely the input vectors of two or more inputsare matched).

The process of dynamically using converged sensor inputs while ignoring(or reducing the relative weight given to) diverged sensor inputs issometimes referred to herein as transmodal input fusion (or simplytransmodal fusion) and can provide substantial advantages overtechniques that simply accept inputs from multiple sensors. Transmodalinput fusion can anticipate or even predict, on a dynamic, real-timebasis, which of the many possible sensor inputs are the appropriatemodal inputs that convey the user's intent to target or operate on areal or virtual object in the user's 3D AR/MR/VR environment.

As will be further explained herein, input modes can include, but arenot limited to, hand or finger gestures, arm gestures, body gestures,head pose, eye gaze, body pose, voice commands, environmental inputs(e.g., position of the user or objects in the user's environment), ashared pose from another user, etc. The sensors used to detect theseinput modes can include, for example, an outward-facing camera (e.g., todetect hand or body gestures), an inward-facing camera (e.g., to detecteye gaze), an inertial measurement unit (IMU, e.g., an accelerometer, agravimeter, a magnetometer), an electromagnetic tracking sensor system,a global positioning system (GPS) sensor, a radar or lidar sensor, etc.(see, e.g., the description of examples of sensors with reference toFIGS. 2A and 2B).

As another example, when a user says “move that there”, the wearablesystem can use a combination of head pose, eye gaze, hand gestures,along with other environmental factors (e.g., the user's location or thelocation of objects around the user), in combination with the voicecommand to determine which object should be moved (e.g., which object is“that”) and which destination is intended (e.g., “there”) in response toan appropriate dynamic selection of these multiple inputs.

As will further be described herein, the techniques for transmodalinputs are not merely an aggregation of multiple user input modes.Rather, the wearable system employing such transmodal techniques canadvantageously support the added depth dimension in 3D (as compared totraditional 2D interactions) provided in the wearable system. The addeddimension not only enables additional types of user interactions (e.g.,rotations, or movements along the additional axis in a Cartesiancoordinate system), but also requires a high degree of precision of auser input to provide the correct outcome.

The user inputs for interacting with virtual objects, however, are notalways accurate due to a user's limitations on motor controls. Althoughtraditional input techniques can calibrate and adjust to theinaccuracies of a user's motor controls in 2D space, such inaccuraciesare magnified in 3D space due to the added dimension. Traditional inputmethods, such as keyboard input, however, are not well suited foradjusting such inaccuracies in 3D space. One benefit provided by thetransmodal input techniques (among other benefits) is to adapt inputmethods into fluid and more accurate interactions with objects in the 3Dspace.

Accordingly, embodiments of the transmodal input techniques candynamically monitor which input modes have converged and use this set ofconverged input modes to more accurately determine or predict that theuser intends to interact with that target. Embodiments of the transmodalinput techniques can dynamically monitor which input modes have diverged(e.g., indicative of an input mode no longer being relevant to apotential target) and cease using these diverged input modes (or reducethe weight given to diverged input modes in comparison to convergedinput modes). The group of sensor input modes that have converged istypically temporary and continuously changing. For example, differentsensor input modes dynamically converge and diverge as the user moveshis or her hands, body, head, or eyes, while providing user input on atotem or using voice commands. Thus, a potential advantage of transmodalinput techniques is that just the right set of sensor input modes areused at any particular time or for any particular target object in the3D environment. In some embodiments, the system may assign a greaterweight (than normally assigned) to a given input based on aphysiological context. As an example, the system may determine that auser is trying to grasp and move a virtual object. In response, thesystem may assign a greater weight to a hand pose input, and less weightto other inputs such as an eye gaze input. The system may also time anyshift in input weighting in a suitable manner. As an example, the systemmay shift weight to the hand pose input as the hand pose converges onthe virtual object.

In addition, advantageously, in some embodiments, the techniquesdescribed herein can reduce the hardware requirements and cost of thewearable system. For example, a wearable device may use low resolutioneye-tracking cameras in connection with a voice command or a head poseto execute a task (e.g., by determining that some or all of these inputmodes have converged on a target object) rather than employ a highresolution eye-tracking camera (which can be expensive and complex toutilize) by itself to determine the task. In this example, the use ofthe user's voice command can compensate for the lower resolution atwhich the eye-tracking is performed. Accordingly, transmodalcombinations of a plurality of user input modes, allowing for dynamicselection of which of the plurality of user input modes to be used, canprovide for lower cost, less complex, and more robust user interactionswith AR/VR/MR devices than the use of a single input mode. Additionalbenefits and examples of techniques related to transmodal sensor fusiontechniques for interacting with real or virtual objects are furtherdescribed below with reference to FIGS. 13-59B.

Transmodal fusion techniques can provide substantial advantages,compared to simply aggregation of multiple sensor inputs, forfunctionalities such as, e.g., targeting small objects, targetingobjects in a field of view containing many objects, targeting movingobjects, managing the transition between nearfield, midfield, andfarfield targeting methods, manipulating virtual objects, and so forth.In some implementations, transmodal fusion techniques are referred to asproviding a TAMDI interaction model for: Targeting (e.g., specifying acursor vector toward an object), Activation (e.g., selection of aspecific object or area or volume in the 3D environment), Manipulation(e.g., directly moving or changing a selection), Deactivation (e.g.,disengaging the selection), and Integration (e.g., putting the previousselection back into the environment, if necessary).

Examples of 3D Display of a Wearable System

A wearable system (also referred to herein as an augmented reality (AR)system) can be configured to present 2D or 3D virtual images to a user.The images may be still images, frames of a video, or a video, incombination or the like. The wearable system can include a wearabledevice that can present VR, AR, or MR content in an environment, aloneor in combination, for user interaction. The wearable device can be ahead-mounted device (HMD) which can includes a head-mounted display. Insome situations, the wearable device is referred to interchangeably asan AR device (ARD).

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson. In FIG. 1, an MR scene 100 is depicted wherein a user of an MRtechnology sees a real-world park-like setting 110 featuring people,trees, buildings in the background, and a concrete platform 120. Inaddition to these items, the user of the MR technology also perceivesthat he “sees” a robot statue 130 standing upon the real-world platform120, and a cartoon-like avatar character 140 flying by which seems to bea personification of a bumble bee, even though these elements do notexist in the real world.

In order for the 3D display to produce a true sensation of depth, andmore specifically, a simulated sensation of surface depth, it may bedesirable for each point in the display's visual field to generate anaccommodative response corresponding to its virtual depth. If theaccommodative response to a display point does not correspond to thevirtual depth of that point, as determined by the binocular depth cuesof convergence and stereopsis, the human eye may experience anaccommodation conflict, resulting in unstable imaging, harmful eyestrain, headaches, and, in the absence of accommodation information,almost a complete lack of surface depth.

VR, AR, and MR experiences can be provided by display systems havingdisplays in which images corresponding to a plurality of renderingplanes are provided to a viewer. A rendering plane can correspond to adepth plane or multiple depth planes. The images may be different foreach rendering plane (e.g., provide slightly different presentations ofa scene or object) and may be separately focused by the viewer's eyes,thereby helping to provide the user with depth cues based on theaccommodation of the eye required to bring into focus different imagefeatures for the scene located on different rendering plane or based onobserving different image features on different rendering planes beingout of focus. As discussed elsewhere herein, such depth cues providecredible perceptions of depth.

FIG. 2A illustrates an example of wearable system 200. The wearablesystem 200 includes a display 220, and various mechanical and electronicmodules and systems to support the functioning of display 220. Thedisplay 220 may be coupled to a frame 230, which is wearable by a user,wearer, or viewer 210. The display 220 can be positioned in front of theeyes of the user 210. The display 220 can present AR/VR/MR content to auser. The display 220 can comprise a head mounted display (HMD) that isworn on the head of the user. In some embodiments, a speaker 240 iscoupled to the frame 230 and positioned adjacent the ear canal of theuser (in some embodiments, another speaker, not shown, is positionedadjacent the other ear canal of the user to provide for stereo/shapeablesound control). The display 220 can include an audio sensor 232 (e.g., amicrophone) for detecting an audio stream from the environment on whichto perform voice recognition.

The wearable system 200 can include an outward-facing imaging system 464(shown in FIG. 4) which observes the world in the environment around theuser. The wearable system 200 can also include an inward-facing imagingsystem 462 (shown in FIG. 4) which can track the eye movements of theuser. The inward-facing imaging system may track either one eye'smovements or both eyes' movements. The inward-facing imaging system 462may be attached to the frame 230 and may be in electrical communicationwith the processing modules 260 or 270, which may process imageinformation acquired by the inward-facing imaging system to determine,e.g., the pupil diameters or orientations of the eyes, eye movements oreye pose of the user 210.

As an example, the wearable system 200 can use the outward-facingimaging system 464 or the inward-facing imaging system 462 to acquireimages of a pose of the user (e.g., a gesture). The images may be stillimages, frames of a video, or a video, in combination or the like. Thewearable system 200 can include other sensors such as electromyogram(EMG) sensors that sense signals indicative of the action of musclegroups (see, e.g., the description with reference to FIGS. 60A and 60B).

The display 220 can be operatively coupled 250, such as by a wired leador wireless connectivity, to a local data processing module 260 whichmay be mounted in a variety of configurations, such as fixedly attachedto the frame 230, fixedly attached to a helmet or hat worn by the user,embedded in headphones, or otherwise removably attached to the user 210(e.g., in a backpack-style configuration, in a belt-coupling styleconfiguration).

The local processing and data module 260 may comprise a hardwareprocessor, as well as digital memory, such as non-volatile memory (e.g.,flash memory), both of which may be utilized to assist in theprocessing, caching, and storage of data. The data may include data a)captured from environmental sensors (which may be, e.g., operativelycoupled to the frame 230 or otherwise attached to the user 210), audiosensors 232 (e.g., microphones); or b) acquired or processed usingremote processing module 270 or remote data repository 280, possibly forpassage to the display 220 after such processing or retrieval. The localprocessing and data module 260 may be operatively coupled bycommunication links 262 or 264, such as via wired or wirelesscommunication links, to the remote processing module 270 or remote datarepository 280 such that these remote modules are available as resourcesto the local processing and data module 260. In addition, remoteprocessing module 280 and remote data repository 280 may be operativelycoupled to each other.

In some embodiments, the remote processing module 270 may comprise oneor more processors configured to analyze and process data and/or imageinformation. In some embodiments, the remote data repository 280 maycomprise a digital data storage facility, which may be available throughthe internet or other networking configuration in a “cloud” resourceconfiguration. In some embodiments, all data is stored and allcomputations are performed in the local processing and data module,allowing fully autonomous use from a remote module.

In addition to or in alternative to the components described in FIG. 2Aor FIG. 2B (described below), the wearable system 200 can includeenvironmental sensors to detect objects, stimuli, people, animals,locations, or other aspects of the world around the user. Theenvironmental sensors may include image capture devices (e.g., cameras,inward-facing imaging system, outward-facing imaging system, etc.),microphones, inertial measurement units (IMUs) (e.g., accelerometers,gyroscopes, magnetometers (compasses)), global positioning system (GPS)units, radio devices, altimeters, barometers, chemical sensors, humiditysensors, temperature sensors, external microphones, light sensors (e.g.,light meters), timing devices (e.g., clocks or calendars), or anycombination or subcombination thereof. In certain embodiments, an IMUmay be a 9-axis IMU which can include a triple-axis gyroscope, atriple-axis accelerometer, and a triple-axis magnetometer.

Environmental sensors may also include a variety of physiologicalsensors. These sensors can measure or estimate the user's physiologicalparameters such as heart rate, respiratory rate, galvanic skin response,blood pressure, encephalographic state, and so on. Environmental sensorsmay further include emissions devices configured to receive signals suchas laser, visible light, invisible wavelengths of light, or sound (e.g.,audible sound, ultrasound, or other frequencies). In some embodiments,one or more environmental sensors (e.g., cameras or light sensors) maybe configured to measure the ambient light (e.g., luminance) of theenvironment (e.g., to capture the lighting conditions of theenvironment). Physical contact sensors, such as strain gauges, curbfeelers, or the like, may also be included as environmental sensors.

FIG. 2B illustrates another example of a wearable system 200, whichincludes examples of many sensors. Inputs from any of these sensors canbe used by the system in the transmodal sensor fusion techniquesdescribed herein. A head mounted wearable component 200 is shownoperatively coupled (68) to a local processing and data module (70),such as a belt pack, here using a physical multicore lead which alsofeatures a control and a quick release module (86) as connecting thebelt pack to the head-mounted display. The head mounted wearablecomponent 200 is also referred to with the reference numeral 58 in FIG.2B and in the following. The local processing and data module (70) isoperatively coupled (100) to a hand held component (606), here by awireless connection such as low power Bluetooth; the hand held component(606) may also be operatively coupled (94) directly to the head mountedwearable component (58), such as by a wireless connection such as lowpower Bluetooth. Generally where IMU data is passed to coordinate posedetection of various components, a high-frequency connection isdesirable, such as in the range of hundreds or thousands ofcycles/second or higher; tens of cycles per second may be adequate forelectromagnetic localization sensing, such as by the sensor (604) andtransmitter (602) pairings. Also shown is a global coordinate system(10), representative of fixed objects in the real world around the user,such as a wall (8).

Cloud resources (46) also may be operatively coupled (42, 40, 88, 90) tothe local processing and data module (70), to the head mounted wearablecomponent (58), to resources which may be coupled to the wall (8) orother item fixed relative to the global coordinate system (10),respectively. The resources coupled to the wall (8) or having knownpositions and/or orientations relative to the global coordinate system(10) may include a wireless transceiver (114), an electromagneticemitter (602) and/or receiver (604), a beacon or reflector (112)configured to emit or reflect a given type of radiation, such as aninfrared LED beacon, a cellular network transceiver (110), a RADARemitter or detector (108), a LIDAR emitter or detector (106), a GPStransceiver (118), a poster or marker having a known detectable pattern(122), and a camera (124).

The system 200 can include a depth camera or depth sensor (154), whichmay, for example, be either a stereo triangulation style depth sensor(such as a passive stereo depth sensor, a texture projection stereodepth sensor, or a structured light stereo depth sensor) or a time orflight style depth sensor (such as a LIDAR depth sensor or a modulatedemission depth sensor). The system 200 can include a forward facing“world” camera (124, which may be a grayscale camera, having a sensorcapable of 720p range resolution, for example) as well as a relativelyhigh-resolution “picture camera” (156, which may be a full color camera,having a sensor capable of two megapixel or higher resolution, forexample).

The head mounted wearable component (58) features similar components, asillustrated, in addition to lighting emitters (130) configured to assistthe camera (124) detectors, such as infrared emitters (130) for aninfrared camera (124); also featured on the head mounted wearablecomponent (58) are one or more strain gauges (116), which may be fixedlycoupled to the frame or mechanical platform of the head mounted wearablecomponent (58) and configured to determine deflection of such platformin between components such as electromagnetic receiver sensors (604) ordisplay elements (220), wherein it may be valuable to understand ifbending of the platform has occurred, such as at a thinned portion ofthe platform, such as the portion above the nose on the eyeglasses-likeplatform depicted in FIG. 2B.

The head mounted wearable component (58) also features a processor (128)and one or more IMUs (102). Each of the components preferably isoperatively coupled to the processor (128). The hand held component(606) and local processing and data module (70) are illustratedfeaturing similar components. As shown in FIG. 2B, with so many sensingand connectivity mechanisms, such a system may be utilized to provide avery high level of connectivity, system component integration, andposition/orientation tracking. For example, with such a configuration,the various main mobile components (58, 70, 606) may be localized interms of position relative to the global coordinate system using WiFi,GPS, or Cellular signal triangulation; beacons, electromagnetictracking, RADAR, and LIDAR systems may provide yet further locationand/or orientation information and feedback. Markers and cameras alsomay be utilized to provide further information regarding relative andabsolute position and orientation. For example, the various cameracomponents (124), such as those shown coupled to the head mountedwearable component (58), may be utilized to capture data which may beutilized in simultaneous localization and mapping protocols, or “SLAM”,to determine where the component (58) is and how it is oriented relativeto other components.

The description with reference to FIGS. 2A and 2B describes anillustrative and non-limiting list of types of sensors and input modesthat can be used with the wearable system 200. However, not all of thesesensors or input modes need be used in every embodiment. Further,additional or alternative sensors can be used as well. The choice ofsensors and input modes for a particular embodiment of the wearablesystem 200 can be based on factors such as cost, weight, size,complexity, etc. Many permutations and combinations of sensors and inputmodes are contemplated. Wearable systems including sensors such as thosedescribed with reference to FIGS. 2A and 2B advantageously can utilizetransmodal input fusion techniques described herein to dynamicallyselect a subset of these sensor inputs to assist the user in selecting,targeting, or interacting with real or virtual objects. The subset ofsensor inputs (which is typically less than the set of all possiblesensor inputs) can include sensor inputs that have converged on a targetobject and can exclude (or reduce reliance upon) sensor inputs that havediverged from the subset or have not converged onto the target object.

The human visual system is complicated and providing a realisticperception of depth is challenging. Without being limited by theory, itis believed that viewers of an object may perceive the object as beingthree-dimensional due to a combination of vergence and accommodation.Vergence movements (e.g., rolling movements of the pupils toward or awayfrom each other to converge the lines of sight of the eyes to fixateupon an object) of the two eyes relative to each other are closelyassociated with focusing (or “accommodation”) of the lenses of the eyes.Under normal conditions, changing the focus of the lenses of the eyes,or accommodating the eyes, to change focus from one object to anotherobject at a different distance will automatically cause a matchingchange in vergence to the same distance, under a relationship known asthe “accommodation-vergence reflex.” Likewise, a change in vergence willtrigger a matching change in accommodation, under normal conditions.Display systems that provide a better match between accommodation andvergence may form more realistic and comfortable simulations ofthree-dimensional imagery.

FIG. 3 illustrates aspects of an approach for simulating athree-dimensional imagery using multiple rendering planes. Withreference to FIG. 3, objects at various distances from eyes 302 and 304on the z-axis are accommodated by the eyes 302 and 304 so that thoseobjects are in focus. The eyes 302 and 304 assume particularaccommodated states to bring into focus objects at different distancesalong the z-axis. Consequently, a particular accommodated state may besaid to be associated with a particular one of rendering planes 306,with has an associated focal distance, such that objects or parts ofobjects in a particular rendering plane are in focus when the eye is inthe accommodated state for that rendering plane. In some embodiments,three-dimensional imagery may be simulated by providing differentpresentations of an image for each of the eyes 302 and 304, and also byproviding different presentations of the image corresponding to each ofthe rendering planes. While shown as being separate for clarity ofillustration, it will be appreciated that the fields of view of the eyes302 and 304 may overlap, for example, as distance along the z-axisincreases. In addition, while shown as flat for the ease ofillustration, it will be appreciated that the contours of a renderingplane may be curved in physical space, such that all features in arendering plane are in focus with the eye in a particular accommodatedstate. Without being limited by theory, it is believed that the humaneye typically can interpret a finite number of rendering planes toprovide depth perception. Consequently, a highly believable simulationof perceived depth may be achieved by providing, to the eye, differentpresentations of an image corresponding to each of these limited numberof rendering planes.

Waveguide Stack Assembly

FIG. 4 illustrates an example of a waveguide stack for outputting imageinformation to a user. A wearable system 400 includes a stack ofwaveguides, or stacked waveguide assembly 480 that may be utilized toprovide three-dimensional perception to the eye/brain using a pluralityof waveguides 432 b, 434 b, 436 b, 438 b, 4400 b. In some embodiments,the wearable system 400 may correspond to wearable system 200 of FIG. 2Aor 2B, with FIG. 4 schematically showing some parts of that wearablesystem 200 in greater detail. For example, in some embodiments, thewaveguide assembly 480 may be integrated into the display 220 of FIGS.2A and 2B.

With continued reference to FIG. 4, the waveguide assembly 480 may alsoinclude a plurality of features 458, 456, 454, 452 between thewaveguides. In some embodiments, the features 458, 456, 454, 452 may belenses. In other embodiments, the features 458, 456, 454, 452 may not belenses. Rather, they may simply be spacers (e.g., cladding layers orstructures for forming air gaps).

The waveguides 432 b, 434 b, 436 b, 438 b, 440 b or the plurality oflenses 458, 456, 454, 452 may be configured to send image information tothe eye with various levels of wavefront curvature or light raydivergence. Each waveguide level may be associated with a particularrendering plane and may be configured to output image informationcorresponding to that rendering plane. Image injection devices 420, 422,424, 426, 428 may be utilized to inject image information into thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b, each of which may beconfigured to distribute incoming light across each respectivewaveguide, for output toward the eye 410. Light exits an output surfaceof the image injection devices 420, 422, 424, 426, 428 and is injectedinto a corresponding input edge of the waveguides 440 b, 438 b, 436 b,434 b, 432 b. In some embodiments, a single beam of light (e.g., acollimated beam) may be injected into each waveguide to output an entirefield of cloned collimated beams that are directed toward the eye 410 atparticular angles (and amounts of divergence) corresponding to therendering plane associated with a particular waveguide.

In some embodiments, the image injection devices 420, 422, 424, 426, 428are discrete displays that each produce image information for injectioninto a corresponding waveguide 440 b, 438 b, 436 b, 434 b, 432 b,respectively. In some other embodiments, the image injection devices420, 422, 424, 426, 428 are the output ends of a single multiplexeddisplay which may, e.g., pipe image information via one or more opticalconduits (such as fiber optic cables) to each of the image injectiondevices 420, 422, 424, 426, 428.

A controller 460 controls the operation of the stacked waveguideassembly 480 and the image injection devices 420, 422, 424, 426, 428.The controller 460 includes programming (e.g., instructions in anon-transitory computer-readable medium) that regulates the timing andprovision of image information to the waveguides 440 b, 438 b, 436 b,434 b, 432 b. In some embodiments, the controller 460 may be a singleintegral device, or a distributed system connected by wired or wirelesscommunication channels. The controller 460 may be part of the processingmodules 260 or 270 (illustrated in FIGS. 2A, 2B) in some embodiments.

The waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be configured topropagate light within each respective waveguide by total internalreflection (TIR). The waveguides 440 b, 438 b, 436 b, 434 b, 432 b mayeach be planar or have another shape (e.g., curved), with major top andbottom surfaces and edges extending between those major top and bottomsurfaces. In the illustrated configuration, the waveguides 440 b, 438 b,436 b, 434 b, 432 b may each include light extracting optical elements440 a, 438 a, 436 a, 434 a, 432 a that are configured to extract lightout of a waveguide by redirecting the light, propagating within eachrespective waveguide, out of the waveguide to output image informationto the eye 410. Extracted light may also be referred to as outcoupledlight, and light extracting optical elements may also be referred to asoutcoupling optical elements. An extracted beam of light is outputted bythe waveguide at locations at which the light propagating in thewaveguide strikes a light redirecting element. The light extractingoptical elements (440 a, 438 a, 436 a, 434 a, 432 a) may, for example,be reflective or diffractive optical features. While illustrateddisposed at the bottom major surfaces of the waveguides 440 b, 438 b,436 b, 434 b, 432 b for ease of description and drawing clarity, in someembodiments, the light extracting optical elements 440 a, 438 a, 436 a,434 a, 432 a may be disposed at the top or bottom major surfaces, or maybe disposed directly in the volume of the waveguides 440 b, 438 b, 436b, 434 b, 432 b. In some embodiments, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be formed in a layer ofmaterial that is attached to a transparent substrate to form thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some other embodiments,the waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be a monolithicpiece of material and the light extracting optical elements 440 a, 438a, 436 a, 434 a, 432 a may be formed on a surface or in the interior ofthat piece of material.

With continued reference to FIG. 4, as discussed herein, each waveguide440 b, 438 b, 436 b, 434 b, 432 b is configured to output light to forman image corresponding to a particular rendering plane. For example, thewaveguide 432 b nearest the eye may be configured to deliver collimatedlight, as injected into such waveguide 432 b, to the eye 410. Thecollimated light may be representative of the optical infinity focalplane. The next waveguide up 434 b may be configured to send outcollimated light which passes through the first lens 452 (e.g., anegative lens) before it can reach the eye 410. First lens 452 may beconfigured to create a slight convex wavefront curvature so that theeye/brain interprets light coming from that next waveguide up 434 b ascoming from a first focal plane closer inward toward the eye 410 fromoptical infinity. Similarly, the third up waveguide 436 b passes itsoutput light through both the first lens 452 and second lens 454 beforereaching the eye 410. The combined optical power of the first and secondlenses 452 and 454 may be configured to create another incrementalamount of wavefront curvature so that the eye/brain interprets lightcoming from the third waveguide 436 b as coming from a second focalplane that is even closer inward toward the person from optical infinitythan was light from the next waveguide up 434 b.

The other waveguide layers (e.g., waveguides 438 b, 440 b) and lenses(e.g., lenses 456, 458) are similarly configured, with the highestwaveguide 440 b in the stack sending its output through all of thelenses between it and the eye for an aggregate focal powerrepresentative of the closest focal plane to the person. To compensatefor the stack of lenses 458, 456, 454, 452 when viewing/interpretinglight coming from the world 470 on the other side of the stackedwaveguide assembly 480, a compensating lens layer 430 may be disposed atthe top of the stack to compensate for the aggregate power of the lensstack 458, 456, 454, 452 below. Such a configuration provides as manyperceived focal planes as there are available waveguide/lens pairings.Both the light extracting optical elements of the waveguides and thefocusing aspects of the lenses may be static (e.g., not dynamic orelectro-active). In some alternative embodiments, either or both may bedynamic using electro-active features.

With continued reference to FIG. 4, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be configured to bothredirect light out of their respective waveguides and to output thislight with the appropriate amount of divergence or collimation for aparticular rendering plane associated with the waveguide. As a result,waveguides having different associated rendering planes may havedifferent configurations of light extracting optical elements, whichoutput light with a different amount of divergence depending on theassociated rendering plane. In some embodiments, as discussed herein,the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 amay be volumetric or surface features, which may be configured to outputlight at specific angles. For example, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be volume holograms,surface holograms, and/or diffraction gratings. Light extracting opticalelements, such as diffraction gratings, are described in U.S. PatentPublication No. 2015/0178939, published Jun. 25, 2015, which isincorporated by reference herein in its entirety.

In some embodiments, the light extracting optical elements 440 a, 438 a,436 a, 434 a, 432 a are diffractive features that form a diffractionpattern, or “diffractive optical element” (also referred to herein as a“DOE”). Preferably, the DOE has a relatively low diffraction efficiencyso that only a portion of the light of the beam is deflected away towardthe eye 410 with each intersection of the DOE, while the rest continuesto move through a waveguide via total internal reflection. The lightcarrying the image information can thus be divided into a number ofrelated exit beams that exit the waveguide at a multiplicity oflocations and the result is a fairly uniform pattern of exit emissiontoward the eye 304 for this particular collimated beam bouncing aroundwithin a waveguide.

In some embodiments, one or more DOEs may be switchable between “on”state in which they actively diffract, and “off” state in which they donot significantly diffract. For instance, a switchable DOE may comprisea layer of polymer dispersed liquid crystal, in which microdropletscomprise a diffraction pattern in a host medium, and the refractiveindex of the microdroplets can be switched to substantially match therefractive index of the host material (in which case the pattern doesnot appreciably diffract incident light) or the microdroplet can beswitched to an index that does not match that of the host medium (inwhich case the pattern actively diffracts incident light).

In some embodiments, the number and distribution of rendering planes ordepth of field may be varied dynamically based on the pupil sizes ororientations of the eyes of the viewer. Depth of field may changeinversely with a viewer's pupil size. As a result, as the sizes of thepupils of the viewer's eyes decrease, the depth of field increases suchthat one plane that is not discernible because the location of thatplane is beyond the depth of focus of the eye may become discernible andappear more in focus with reduction of pupil size and commensurate withthe increase in depth of field. Likewise, the number of spaced apartrendering planes used to present different images to the viewer may bedecreased with the decreased pupil size. For example, a viewer may notbe able to clearly perceive the details of both a first rendering planeand a second rendering plane at one pupil size without adjusting theaccommodation of the eye away from one rendering plane and to the otherrendering plane. These two rendering planes may, however, besufficiently in focus at the same time to the user at another pupil sizewithout changing accommodation.

In some embodiments, the display system may vary the number ofwaveguides receiving image information based upon determinations ofpupil size or orientation, or upon receiving electrical signalsindicative of particular pupil size or orientation. For example, if theuser's eyes are unable to distinguish between two rendering planesassociated with two waveguides, then the controller 460 may beconfigured or programmed to cease providing image information to one ofthese waveguides. Advantageously, this may reduce the processing burdenon the system, thereby increasing the responsiveness of the system. Inembodiments in which the DOEs for a waveguide are switchable between theon and off states, the DOEs may be switched to the off state when thewaveguide does receive image information.

In some embodiments, it may be desirable to have an exit beam meet thecondition of having a diameter that is less than the diameter of the eyeof a viewer. However, meeting this condition may be challenging in viewof the variability in size of the viewer's pupils. In some embodiments,this condition is met over a wide range of pupil sizes by varying thesize of the exit beam in response to determinations of the size of theviewer's pupil. For example, as the pupil size decreases, the size ofthe exit beam may also decrease. In some embodiments, the exit beam sizemay be varied using a variable aperture.

The wearable system 400 can include an outward-facing imaging system 464(e.g., a digital camera) that images a portion of the world 470. Thisportion of the world 470 may be referred to as the field of view (FOV)of a world camera and the imaging system 464 is sometimes referred to asan FOV camera. The entire region available for viewing or imaging by aviewer may be referred to as the field of regard (FOR). The FOR mayinclude 4π steradians of solid angle surrounding the wearable system 400because the wearer can move his body, head, or eyes to perceivesubstantially any direction in space. In other contexts, the wearer'smovements may be more constricted, and accordingly the wearer's FOR maysubtend a smaller solid angle. Images obtained from the outward-facingimaging system 464 can be used to track gestures made by the user (e.g.,hand or finger gestures), detect objects in the world 470 in front ofthe user, and so forth.

The wearable system 400 can also include an inward-facing imaging system462 (e.g., a digital camera), which observes the movements of the user,such as the eye movements and the facial movements. The inward-facingimaging system 462 may be used to capture images of the eye 410 todetermine the size and/or orientation of the pupil of the eye 304. Theinward-facing imaging system 462 can be used to obtain images for use indetermining the direction the user is looking (e.g., eye pose) or forbiometric identification of the user (e.g., via iris identification). Insome embodiments, at least one camera may be utilized for each eye, toseparately determine the pupil size or eye pose of each eyeindependently, thereby allowing the presentation of image information toeach eye to be dynamically tailored to that eye. In some otherembodiments, the pupil diameter or orientation of only a single eye 410(e.g., using only a single camera per pair of eyes) is determined andassumed to be similar for both eyes of the user. The images obtained bythe inward-facing imaging system 462 may be analyzed to determine theuser's eye pose or mood, which can be used by the wearable system 400 todecide which audio or visual content should be presented to the user.The wearable system 400 may also determine head pose (e.g., headposition or head orientation) using sensors such as IMUs,accelerometers, gyroscopes, etc.

The wearable system 400 can include a user input device 466 by which theuser can input commands to the controller 460 to interact with thewearable system 400. For example, the user input device 466 can includea trackpad, a touchscreen, a joystick, a multiple degree-of-freedom(DOF) controller, a capacitive sensing device, a game controller, akeyboard, a mouse, a directional pad (D-pad), a wand, a haptic device, atotem (e.g., functioning as a virtual user input device), and so forth.A multi-DOF controller can sense user input in some or all possibletranslations (e.g., left/right, forward/backward, or up/down) orrotations (e.g., yaw, pitch, or roll) of the controller. A multi-DOFcontroller which supports the translation movements may be referred toas a 3DOF while a multi-DOF controller which supports the translationsand rotations may be referred to as 6DOF. In some cases, the user mayuse a finger (e.g., a thumb) to press or swipe on a touch-sensitiveinput device to provide input to the wearable system 400 (e.g., toprovide user input to a user interface provided by the wearable system400). The user input device 466 may be held by the user's hand duringthe use of the wearable system 400. The user input device 466 can be inwired or wireless communication with the wearable system 400.

FIG. 5 shows an example of exit beams outputted by a waveguide. Onewaveguide is illustrated, but it will be appreciated that otherwaveguides in the waveguide assembly 480 may function similarly, wherethe waveguide assembly 480 includes multiple waveguides. Light 520 isinjected into the waveguide 432 b at the input edge 432 c of thewaveguide 432 b and propagates within the waveguide 432 b by TIR. Atpoints where the light 520 impinges on the DOE 432 a, a portion of thelight exits the waveguide as exit beams 510. The exit beams 510 areillustrated as substantially parallel but they may also be redirected topropagate to the eye 410 at an angle (e.g., forming divergent exitbeams), depending on the rendering plane associated with the waveguide432 b. It will be appreciated that substantially parallel exit beams maybe indicative of a waveguide with light extracting optical elements thatoutcouple light to form images that appear to be set on a renderingplane at a large distance (e.g., optical infinity) from the eye 410.Other waveguides or other sets of light extracting optical elements mayoutput an exit beam pattern that is more divergent, which would requirethe eye 410 to accommodate to a closer distance to bring it into focuson the retina and would be interpreted by the brain as light from adistance closer to the eye 410 than optical infinity.

FIG. 6 is a schematic diagram showing an optical system including awaveguide apparatus, an optical coupler subsystem to optically couplelight to or from the waveguide apparatus, and a control subsystem, usedin the generation of a multi-focal volumetric display, image, or lightfield. The optical system can include a waveguide apparatus, an opticalcoupler subsystem to optically couple light to or from the waveguideapparatus, and a control subsystem. The optical system can be used togenerate a multi-focal volumetric, image, or light field. The opticalsystem can include one or more primary planar waveguides 632 a (only oneis shown in FIG. 6) and one or more DOEs 632 b associated with each ofat least some of the primary waveguides 632 a. The planar waveguides 632b can be similar to the waveguides 432 b, 434 b, 436 b, 438 b, 440 bdiscussed with reference to FIG. 4. The optical system may employ adistribution waveguide apparatus to relay light along a first axis(vertical or Y-axis in view of FIG. 6), and expand the light's effectiveexit pupil along the first axis (e.g., Y-axis). The distributionwaveguide apparatus may, for example, include a distribution planarwaveguide 622 b and at least one DOE 622 a (illustrated by doubledash-dot line) associated with the distribution planar waveguide 622 b.The distribution planar waveguide 622 b may be similar or identical inat least some respects to the primary planar waveguide 632 b, having adifferent orientation therefrom. Likewise, at least one DOE 622 a may besimilar or identical in at least some respects to the DOE 632 a. Forexample, the distribution planar waveguide 622 b or DOE 622 a may becomprised of the same materials as the primary planar waveguide 632 b orDOE 632 a, respectively. Embodiments of the optical display system 600shown in FIG. 6 can be integrated into the wearable system 200 shown inFIG. 2A or 2B.

The relayed and exit-pupil expanded light may be optically coupled fromthe distribution waveguide apparatus into the one or more primary planarwaveguides 632 b. The primary planar waveguide 632 b can relay lightalong a second axis, preferably orthogonal to first axis (e.g.,horizontal or X-axis in view of FIG. 6). Notably, the second axis can bea non-orthogonal axis to the first axis. The primary planar waveguide632 b expands the light's effective exit pupil along that second axis(e.g., X-axis). For example, the distribution planar waveguide 622 b canrelay and expand light along the vertical or Y-axis, and pass that lightto the primary planar waveguide 632 b which can relay and expand lightalong the horizontal or X-axis.

The optical system may include one or more sources of colored light(e.g., red, green, and blue laser light) 610 which may be opticallycoupled into a proximal end of a single mode optical fiber 640. A distalend of the optical fiber 640 may be threaded or received through ahollow tube 642 of piezoelectric material. The distal end protrudes fromthe tube 642 as fixed-free flexible cantilever 644. The piezoelectrictube 642 can be associated with four quadrant electrodes (notillustrated). The electrodes may, for example, be plated on the outside,outer surface or outer periphery or diameter of the tube 642. A coreelectrode (not illustrated) may also be located in a core, center, innerperiphery or inner diameter of the tube 642.

Drive electronics 650, for example electrically coupled via wires 660,drive opposing pairs of electrodes to bend the piezoelectric tube 642 intwo axes independently. The protruding distal tip of the optical fiber644 has mechanical modes of resonance. The frequencies of resonance candepend upon a diameter, length, and material properties of the opticalfiber 644. By vibrating the piezoelectric tube 642 near a first mode ofmechanical resonance of the fiber cantilever 644, the fiber cantilever644 can be caused to vibrate, and can sweep through large deflections.

By stimulating resonant vibration in two axes, the tip of the fibercantilever 644 is scanned biaxially in an area filling two-dimensional(2D) scan. By modulating an intensity of light source(s) 610 insynchrony with the scan of the fiber cantilever 644, light emerging fromthe fiber cantilever 644 can form an image. Descriptions of such a setup are provided in U.S. Patent Publication No. 2014/0003762, which isincorporated by reference herein in its entirety.

A component of an optical coupler subsystem can collimate the lightemerging from the scanning fiber cantilever 644. The collimated lightcan be reflected by mirrored surface 648 into the narrow distributionplanar waveguide 622 b which contains the at least one diffractiveoptical element (DOE) 622 a. The collimated light can propagatevertically (relative to the view of FIG. 6) along the distributionplanar waveguide 622 b by TIR, and in doing so repeatedly intersectswith the DOE 622 a. The DOE 622 a preferably has a low diffractionefficiency. This can cause a fraction (e.g., 10%) of the light to bediffracted toward an edge of the larger primary planar waveguide 632 bat each point of intersection with the DOE 622 a, and a fraction of thelight to continue on its original trajectory down the length of thedistribution planar waveguide 622 b via TIR.

At each point of intersection with the DOE 622 a, additional light canbe diffracted toward the entrance of the primary waveguide 632 b. Bydividing the incoming light into multiple outcoupled sets, the exitpupil of the light can be expanded vertically by the DOE 4 in thedistribution planar waveguide 622 b. This vertically expanded lightcoupled out of distribution planar waveguide 622 b can enter the edge ofthe primary planar waveguide 632 b.

Light entering primary waveguide 632 b can propagate horizontally(relative to the view of FIG. 6) along the primary waveguide 632 b viaTIR. As the light intersects with DOE 632 a at multiple points as itpropagates horizontally along at least a portion of the length of theprimary waveguide 632 b via TIR. The DOE 632 a may advantageously bedesigned or configured to have a phase profile that is a summation of alinear diffraction pattern and a radially symmetric diffractive pattern,to produce both deflection and focusing of the light. The DOE 632 a mayadvantageously have a low diffraction efficiency (e.g., 10%), so thatonly a portion of the light of the beam is deflected toward the eye ofthe view with each intersection of the DOE 632 a while the rest of thelight continues to propagate through the primary waveguide 632 b viaTIR.

At each point of intersection between the propagating light and the DOE632 a, a fraction of the light is diffracted toward the adjacent face ofthe primary waveguide 632 b allowing the light to escape the TIR, andemerge from the face of the primary waveguide 632 b. In someembodiments, the radially symmetric diffraction pattern of the DOE 632 aadditionally imparts a focus level to the diffracted light, both shapingthe light wavefront (e.g., imparting a curvature) of the individual beamas well as steering the beam at an angle that matches the designed focuslevel.

Accordingly, these different pathways can cause the light to be coupledout of the primary planar waveguide 632 b by a multiplicity of DOEs 632a at different angles, focus levels, and/or yielding different fillpatterns at the exit pupil. Different fill patterns at the exit pupilcan be beneficially used to create a light field display with multiplerendering planes. Each layer in the waveguide assembly or a set oflayers (e.g., 3 layers) in the stack may be employed to generate arespective color (e.g., red, blue, green). Thus, for example, a firstset of three adjacent layers may be employed to respectively producered, blue and green light at a first focal depth. A second set of threeadjacent layers may be employed to respectively produce red, blue andgreen light at a second focal depth. Multiple sets may be employed togenerate a full 3D or 4D color image light field with various focaldepths.

Other Components of the Wearable System

In many implementations, the wearable system may include othercomponents in addition or in alternative to the components of thewearable system described above. The wearable system may, for example,include one or more haptic devices or components. The haptic devices orcomponents may be operable to provide a tactile sensation to a user. Forexample, the haptic devices or components may provide a tactilesensation of pressure or texture when touching virtual content (e.g.,virtual objects, virtual tools, other virtual constructs). The tactilesensation may replicate a feel of a physical object which a virtualobject represents, or may replicate a feel of an imagined object orcharacter (e.g., a dragon) which the virtual content represents. In someimplementations, haptic devices or components may be worn by the user(e.g., a user wearable glove). In some implementations, haptic devicesor components may be held by the user.

The wearable system may, for example, include one or more physicalobjects which are manipulable by the user to allow input or interactionwith the wearable system. These physical objects may be referred toherein as totems. Some totems may take the form of inanimate objects,such as for example, a piece of metal or plastic, a wall, a surface oftable. In certain implementations, the totems may not actually have anyphysical input structures (e.g., keys, triggers, joystick, trackball,rocker switch). Instead, the totem may simply provide a physicalsurface, and the wearable system may render a user interface so as toappear to a user to be on one or more surfaces of the totem. Forexample, the wearable system may render an image of a computer keyboardand trackpad to appear to reside on one or more surfaces of a totem. Forexample, the wearable system may render a virtual computer keyboard andvirtual trackpad to appear on a surface of a thin rectangular plate ofaluminum which serves as a totem. The rectangular plate does not itselfhave any physical keys or trackpad or sensors. However, the wearablesystem may detect user manipulation or interaction or touches with therectangular plate as selections or inputs made via the virtual keyboardor virtual trackpad. The user input device 466 (shown in FIG. 4) may bean embodiment of a totem, which may include a trackpad, a touchpad, atrigger, a joystick, a trackball, a rocker or virtual switch, a mouse, akeyboard, a multi-degree-of-freedom controller, or another physicalinput device. A user may use the totem, alone or in combination withposes, to interact with the wearable system or other users.

Examples of haptic devices and totems usable with the wearable devices,HMD, and display systems of the present disclosure are described in U.S.Patent Publication No. 2015/0016777, which is incorporated by referenceherein in its entirety.

Example Wearable Systems, Environments, and Interfaces

A wearable system may employ various mapping related techniques in orderto achieve high depth of field in the rendered light fields. In mappingout the virtual world, it is advantageous to know all the features andpoints in the real world to accurately portray virtual objects inrelation to the real world. To this end, FOV images captured from usersof the wearable system can be added to a world model by including newpictures that convey information about various points and features ofthe real world. For example, the wearable system can collect a set ofmap points (such as 2D points or 3D points) and find new map points torender a more accurate version of the world model. The world model of afirst user can be communicated (e.g., over a network such as a cloudnetwork) to a second user so that the second user can experience theworld surrounding the first user.

FIG. 7 is a block diagram of an example of an MR environment 700. The MRenvironment 700 may be configured to receive input (e.g., visual input702 from the user's wearable system, stationary input 704 such as roomcameras, sensory input 706 from various sensors, gestures, totems, eyetracking, user input from the user input device 466 etc.) from one ormore user wearable systems (e.g., wearable system 200 or display system220) or stationary room systems (e.g., room cameras, etc.). The wearablesystems can use various sensors (e.g., accelerometers, gyroscopes,temperature sensors, movement sensors, depth sensors, GPS sensors,inward-facing imaging system, outward-facing imaging system, etc.) todetermine the location and various other attributes of the environmentof the user. This information may further be supplemented withinformation from stationary cameras in the room that may provide imagesor various cues from a different point of view. The image data acquiredby the cameras (such as the room cameras and/or the cameras of theoutward-facing imaging system) may be reduced to a set of mappingpoints.

One or more object recognizers 708 can crawl through the received data(e.g., the collection of points) and recognize or map points, tagimages, attach semantic information to objects with the help of a mapdatabase 710. The map database 710 may comprise various points collectedover time and their corresponding objects. The various devices and themap database can be connected to each other through a network (e.g.,LAN, WAN, etc.) to access the cloud.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects in anenvironment. For example, the object recognizers can recognize faces,persons, windows, walls, user input devices, televisions, other objectsin the user's environment, etc. One or more object recognizers may bespecialized for object with certain characteristics. For example, theobject recognizer 708 a may be used to recognize faces, while anotherobject recognizer may be used recognize totems, while another objectrecognizer may be used to recognize hand, finger, arm, or body gestures.

The object recognitions may be performed using a variety of computervision techniques. For example, the wearable system can analyze theimages acquired by the outward-facing imaging system 464 (shown in FIG.4) to perform scene reconstruction, event detection, video tracking,object recognition, object pose estimation, learning, indexing, motionestimation, or image restoration, etc. One or more computer visionalgorithms may be used to perform these tasks. Non-limiting examples ofcomputer vision algorithms include: Scale-invariant feature transform(SIFT), speeded up robust features (SURF), oriented FAST and rotatedBRIEF (ORB), binary robust invariant scalable keypoints (BRISK), fastretina keypoint (FREAK), Viola-Jones algorithm, Eigenfaces approach,Lucas-Kanade algorithm, Horn-Schunk algorithm, Mean-shift algorithm,visual simultaneous location and mapping (vSLAM) techniques, asequential Bayesian estimator (e.g., Kalman filter, extended Kalmanfilter, etc.), bundle adjustment, Adaptive thresholding (and otherthresholding techniques), Iterative Closest Point (ICP), Semi GlobalMatching (SGM), Semi Global Block Matching (SGBM), Feature PointHistograms, various machine learning algorithms (such as e.g., supportvector machine, k-nearest neighbors algorithm, Naive Bayes, neuralnetwork (including convolutional or deep neural networks), or othersupervised/unsupervised models, etc.), and so forth.

The object recognitions can additionally or alternatively be performedby a variety of machine learning algorithms. Once trained, the machinelearning algorithm can be stored by the HMD. Some examples of machinelearning algorithms can include supervised or non-supervised machinelearning algorithms, including regression algorithms (such as, forexample, Ordinary Least Squares Regression), instance-based algorithms(such as, for example, Learning Vector Quantization), decision treealgorithms (such as, for example, classification and regression trees),Bayesian algorithms (such as, for example, Naive Bayes), clusteringalgorithms (such as, for example, k-means clustering), association rulelearning algorithms (such as, for example, a-priori algorithms),artificial neural network algorithms (such as, for example, Perceptron),deep learning algorithms (such as, for example, Deep Boltzmann Machine,or deep neural network), dimensionality reduction algorithms (such as,for example, Principal Component Analysis), ensemble algorithms (suchas, for example, Stacked Generalization), and/or other machine learningalgorithms. In some embodiments, individual models can be customized forindividual data sets. For example, the wearable device can generate orstore a base model. The base model may be used as a starting point togenerate additional models specific to a data type (e.g., a particularuser in the telepresence session), a data set (e.g., a set of additionalimages obtained of the user in the telepresence session), conditionalsituations, or other variations. In some embodiments, the wearable HMDcan be configured to utilize a plurality of techniques to generatemodels for analysis of the aggregated data. Other techniques may includeusing pre-defined thresholds or data values.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects andsupplement objects with semantic information to give life to theobjects. For example, if the object recognizer recognizes a set ofpoints to be a door, the system may attach some semantic information(e.g., the door has a hinge and has a 90 degree movement about thehinge). If the object recognizer recognizes a set of points to be amirror, the system may attach semantic information that the mirror has areflective surface that can reflect images of objects in the room. Overtime the map database grows as the system (which may reside locally ormay be accessible through a wireless network) accumulates more data fromthe world. Once the objects are recognized, the information may betransmitted to one or more wearable systems. For example, the MRenvironment 700 may include information about a scene happening inCalifornia. The environment 700 may be transmitted to one or more usersin New York. Based on data received from an FOV camera and other inputs,the object recognizers and other software components can map the pointscollected from the various images, recognize objects etc., such that thescene may be accurately “passed over” to a second user, who may be in adifferent part of the world. The environment 700 may also use atopological map for localization purposes.

FIG. 8 is a process flow diagram of an example of a method 800 ofrendering virtual content in relation to recognized objects. The method800 describes how a virtual scene may be represented to a user of thewearable system. The user may be geographically remote from the scene.For example, the user may be New York, but may want to view a scene thatis presently going on in California, or may want to go on a walk with afriend who resides in California.

At block 810, the wearable system may receive input from the user andother users regarding the environment of the user. This may be achievedthrough various input devices, and knowledge already possessed in themap database. The user's FOV camera, sensors, GPS, eye tracking, etc.,convey information to the system at block 810. The system may determinesparse points based on this information at block 820. The sparse pointsmay be used in determining pose data (e.g., head pose, eye pose, bodypose, or hand gestures) that can be used in displaying and understandingthe orientation and position of various objects in the user'ssurroundings. The object recognizers 708 a-708 n may crawl through thesecollected points and recognize one or more objects using a map databaseat block 830. This information may then be conveyed to the user'sindividual wearable system at block 840, and the desired virtual scenemay be accordingly displayed to the user at block 850. For example, thedesired virtual scene (e.g., user in CA) may be displayed at theappropriate orientation, position, etc., in relation to the variousobjects and other surroundings of the user in New York.

FIG. 9 is a block diagram of another example of a wearable system. Inthis example, the wearable system 900 comprises a map, which may includemap data for the world. The map may partly reside locally on thewearable system, and may partly reside at networked storage locationsaccessible by wired or wireless network (e.g., in a cloud system). Apose process 910 (e.g., head or eye pose) may be executed on thewearable computing architecture (e.g., processing module 260 orcontroller 460) and utilize data from the map to determine position andorientation of the wearable computing hardware or user. Pose data may becomputed from data collected on the fly as the user is experiencing thesystem and operating in the world. The data may comprise images, datafrom sensors (such as inertial measurement units, which generallycomprise accelerometer and gyroscope components) and surface informationpertinent to objects in the real or virtual environment.

A sparse point representation may be the output of a simultaneouslocalization and mapping (SLAM or V-SLAM, referring to a configurationwherein the input is images/visual only) process. The system can beconfigured to not only find out where in the world the variouscomponents are, but what the world is made of. Pose may be a buildingblock that achieves many goals, including populating the map and usingthe data from the map.

In one embodiment, a sparse point position may not be completelyadequate on its own, and further information may be needed to produce amultifocal AR, VR, or MR experience. Dense representations, generallyreferring to depth map information, may be utilized to fill this gap atleast in part. Such information may be computed from a process referredto as Stereo 940, wherein depth information is determined using atechnique such as triangulation or time-of-flight sensing. Imageinformation and active patterns (such as infrared patterns created usingactive projectors) may serve as input to the Stereo process 940. Asignificant amount of depth map information may be fused together, andsome of this may be summarized with a surface representation. Forexample, mathematically definable surfaces may be efficient (e.g.,relative to a large point cloud) and digestible inputs to otherprocessing devices like game engines. Thus, the output of the stereoprocess (e.g., a depth map) 940 may be combined in the fusion process930. Pose may be an input to this fusion process 930 as well, and theoutput of fusion 930 becomes an input to populating the map process 920.Sub-surfaces may connect with each other, such as in topographicalmapping, to form larger surfaces, and the map becomes a large hybrid ofpoints and surfaces.

To resolve various aspects in a mixed reality process 960, variousinputs may be utilized. For example, in the embodiment depicted in FIG.9, Game parameters may be inputs to determine that the user of thesystem is playing a monster battling game with one or more monsters atvarious locations, monsters dying or running away under variousconditions (such as if the user shoots the monster), walls or otherobjects at various locations, and the like. The world map may includeinformation regarding where such objects are relative to each other, tobe another valuable input to mixed reality. Pose relative to the worldbecomes an input as well and plays a key role to almost any interactivesystem.

Controls or inputs from the user are another input to the wearablesystem 900. As described herein, user inputs can include visual input,gestures, totems, audio input, sensory input, etc. In order to movearound or play a game, for example, the user may need to instruct thewearable system 900 regarding what he or she wants to do. Beyond justmoving oneself in space, there are various forms of user controls thatmay be utilized. In one embodiment, a totem (e.g. a user input device),or an object such as a toy gun may be held by the user and tracked bythe system. The system preferably will be configured to know that theuser is holding the item and understand what kind of interaction theuser is having with the item (e.g., if the totem or object is a gun, thesystem may be configured to understand location and orientation, as wellas whether the user is clicking a trigger or other sensed button orelement which may be equipped with a sensor, such as an IMU, which mayassist in determining what is going on, even when such activity is notwithin the field of view of any of the cameras.)

Hand gesture tracking or recognition may also provide input information.The wearable system 900 may be configured to track and interpret handgestures for button presses, for gesturing left or right, stop, grab,hold, etc. For example, in one configuration, the user may want to flipthrough emails or a calendar in a non-gaming environment, or do a “fistbump” with another person or player. The wearable system 900 may beconfigured to leverage a minimum amount of hand gesture, which may ormay not be dynamic. For example, the gestures may be simple staticgestures like open hand for stop, thumbs up for ok, thumbs down for notok; or a hand flip right, or left, or up/down for directional commands.

Eye tracking is another input (e.g., tracking where the user is lookingto control the display technology to render at a specific depth orrange). In one embodiment, vergence of the eyes may be determined usingtriangulation, and then using a vergence/accommodation model developedfor that particular person, accommodation may be determined.

Voice recognition is another input, which can be used alone or incombination with other inputs (e.g., totem tracking, eye tracking,gesture tracking, etc.). The system 900 can include an audio sensor 232(e.g., a microphone) that receives an audio stream from the environment.The received audio stream can be processed (e.g., by processing modules260, 270 or central server 1650) to recognize a user's voice (from othervoices or background audio), to extract commands, subjects, parameters,etc. from the audio stream. For example, the system 900 may identifyfrom an audio stream that the phrase “move that there” was said,identify that this phrase was said by the wearer of the system 900(rather than another person in the user's environment), and extract fromthe phrase that there is an executable command (“move”) and an object tobe moved (“that”) to a location (“there”). The object to be operatedupon by the command may be referred to as the subject of the command,and other information provided as a parameter to the command. In thisexample, the location of where the object is to be moved is a parameterfor the move command. Parameters can include, for example, a location, atime, other objects to be interacted with (e.g., “move that next to thered chair” or “give the magic wand to Linda”), how the command is to beexecuted (e.g., “play my music using the upstairs speakers”), and soforth.

As another example, the system 900 can process an audio stream withspeech recognition techniques to input a string of text or to modifycontent of a text. The system 900 can incorporate speaker recognitiontechnology to determine who is speaking as well as speech recognitiontechnology to determine what is being said. Voice recognition techniquescan include hidden Markov models, Gaussian mixture models, patternmatching algorithms, neural networks, matrix representation, VectorQuantization, speaker diarisation, decision trees, and dynamic timewarping (DTW) techniques, alone or in combination. Voice recognitiontechniques can also include anti-speaker techniques, such as cohortmodels, and world models. Spectral features may be used in representingspeaker characteristics.

With regard to the camera systems, the example wearable system 900 shownin FIG. 9 can include three pairs of cameras: a relative wide FOV orpassive SLAM pair of cameras arranged to the sides of the user's face, adifferent pair of cameras oriented in front of the user to handle thestereo imaging process 940 and also to capture hand gestures andtotem/object tracking in front of the user's face. The FOV cameras andthe pair of cameras for the stereo process 940 may be a part of theoutward-facing imaging system 464 (shown in FIG. 4). The wearable system900 can include eye tracking cameras (which may be a part of aninward-facing imaging system 462 shown in FIG. 4) oriented toward theeyes of the user in order to triangulate eye vectors and otherinformation. The wearable system 900 may also comprise one or moretextured light projectors (such as infrared (IR) projectors) to injecttexture into a scene.

FIG. 10 is a process flow diagram of an example of a method 1000 fordetermining user input to a wearable system. In this example, the usermay interact with a totem. The user may have multiple totems. Forexample, the user may have designated one totem for a social mediaapplication, another totem for playing games, etc. At block 1010, thewearable system may detect a motion of a totem. The movement of thetotem may be recognized through the outward facing system or may bedetected through sensors (e.g., haptic glove, image sensors, handtracking devices, eye-tracking cameras, head pose sensors, etc.).

Based at least partly on the detected gesture, eye pose, head pose, orinput through the totem, the wearable system detects a position,orientation, and/or movement of the totem (or the user's eyes or head orgestures) with respect to a reference frame, at block 1020. Thereference frame may be a set of map points based on which the wearablesystem translates the movement of the totem (or the user) to an actionor command. At block 1030, the user's interaction with the totem ismapped. Based on the mapping of the user interaction with respect to thereference frame 1020, the system determines the user input at block1040.

For example, the user may move a totem or physical object back and forthto signify turning a virtual page and moving on to a next page or movingfrom one user interface (UI) display screen to another UI screen. Asanother example, the user may move their head or eyes to look atdifferent real or virtual objects in the user's FOR. If the user's gazeat a particular real or virtual object is longer than a threshold time,the real or virtual object may be selected as the user input. In someimplementations, the vergence of the user's eyes can be tracked and anaccommodation/vergence model can be used to determine the accommodationstate of the user's eyes, which provides information on a renderingplane on which the user is focusing. In some implementations, thewearable system can use cone casting techniques to determine which realor virtual objects are along the direction of the user's head pose oreye pose. Cone casting techniques, described generally, can project aninvisible cone in the direction the user is looking and identify anyobjects that intersect with the cone. The cone castings can involvecasting thin, pencil rays with substantially little transverse width orcasting rays with substantial transverse width (e.g., cones or frustums)from an AR display (of the wearable system) toward physical or virtualobjects. Cone casting with a single ray may also be referred to as raycasting. Detailed examples of cone casting techniques are described inU.S. application Ser. No. 15/473,444, titled “Interactions with 3DVirtual Objects Using Poses and Multiple-DOF Controllers”, filed Mar.29, 2017, the disclosure of which is hereby incorporated by reference inits entirety.

The user interface may be projected by the display system as describedherein (such as the display 220 in FIG. 2A or 2B). It may also bedisplayed using a variety of other techniques such as one or moreprojectors. The projectors may project images onto a physical objectsuch as a canvas or a globe. Interactions with user interface may betracked using one or more cameras external to the system or part of thesystem (such as, e.g., using the inward-facing imaging system 462 or theoutward-facing imaging system 464).

FIG. 11 is a process flow diagram of an example of a method 1100 forinteracting with a virtual user interface. The method 1100 may beperformed by the wearable system described herein.

At block 1110, the wearable system may identify a particular UI. Thetype of UI may be predetermined by the user. The wearable system mayidentify that a particular UI needs to be populated based on a userinput (e.g., gesture, visual data, audio data, sensory data, directcommand, etc.). At block 1120, the wearable system may generate data forthe virtual UI. For example, data associated with the confines, generalstructure, shape of the UI etc., may be generated. In addition, thewearable system may determine map coordinates of the user's physicallocation so that the wearable system can display the UI in relation tothe user's physical location. For example, if the UI is body centric,the wearable system may determine the coordinates of the user's physicalstance, head pose, or eye pose such that a ring UI can be displayedaround the user or a planar UI can be displayed on a wall or in front ofthe user. If the UI is hand centric, the map coordinates of the user'shands may be determined. These map points may be derived through datareceived through the FOV cameras, sensory input, or any other type ofcollected data.

At block 1130, the wearable system may send the data to the display fromthe cloud or the data may be sent from a local database to the displaycomponents. At block 1140, the UI is displayed to the user based on thesent data. For example, a light field display can project the virtual UIinto one or both of the user's eyes. Once the virtual UI has beencreated, the wearable system may simply wait for a command from the userto generate more virtual content on the virtual UI at block 1150. Forexample, the UI may be a body centric ring around the user's body. Thewearable system may then wait for the command (a gesture, a head or eyemovement, input from a user input device, etc.), and if it is recognized(block 1160), virtual content associated with the command may bedisplayed to the user (block 1170). As an example, the wearable systemmay wait for user's hand gestures before mixing multiple steam tracks.

Additional examples of wearable systems, UIs, and user experiences (UX)are described in U.S. Patent Publication No. 2015/0016777, which isincorporated by reference herein in its entirety.

Examples Objects in the Field of Regard (FOR) and Field of View (FOV)

FIG. 12A schematically illustrates an example of a field of regard (FOR)1200, a field of view (FOV) of a world camera 1270, a field of view of auser 1250, and a field of fixation of a user 1290. As described withreference to FIG. 4, the FOR 1200 comprises a portion of the environmentaround the user that is capable of being perceived by the user via thewearable system. The FOR may include 4 π steradians of solid anglesurrounding the wearable system because the wearer can move his body,head, or eyes to perceive substantially any direction in space. In othercontexts, the wearer's movements may be more constricted, andaccordingly the wearer's FOR may subtend a smaller solid angle.

The field of view of a world camera 1270 can include a portion of theuser's FOR that is currently observed by an outward-facing imagingsystem 464. With reference to FIG. 4, the field of view of a worldcamera 1270 may include the world 470 that is observed by the wearablesystem 400 at a given time. The size of the FOV of the world camera 1270may depend on the optical characteristics of the outward-facing imagingsystem 464. For example, the outward-facing imaging system 464 caninclude a wide angle camera that can image a 190 degree space around theuser. In certain implementations, the FOV of the world camera 1270 maybe larger than or equal to a natural FOV of a user's eyes.

The FOV of a user 1250 can comprise the portion of the FOR 1200 that auser perceives at a given time. The FOV can depend on the size oroptical characteristics of the display of a wearable device. Forexample, an AR/MR display may include optics that provides AR/MRfunctionality when the user looks through a particular portion of thedisplay. The FOV 1250 may correspond to the solid angle that isperceivable by the user when looking through an AR/MR display such as,e.g., the stacked waveguide assembly 480 (FIG. 4) or the planarwaveguide 600 (FIG. 6). In certain embodiments, the FOV of a user 1250may be smaller than the natural FOV of the user's eyes.

The wearable system can also determine a user's field of fixation 1290.The field of fixation 1290 can include a portion of the FOV 1250 atwhich the user's eyes can fixate (e.g., maintain visual gaze at thatportion). The field of fixation 1290 may correspond to the fovea regionof the eyes that a light falls on. The field of fixation 1290 can besmaller than the FOV 1250 of a user, for example, the field of fixationmay be a few degrees to about 5 degrees across. As a result, even thoughthe user can perceive some virtual objects in the FOV 1250 that are notin the field of fixation 1290 but which are in a peripheral field ofview of the user.

FIG. 12B schematically illustrates an example of virtual objects in auser's field of view (FOV) and virtual objects in a field of regard(FOR). In FIG. 12B, the FOR 1200 can contain a group of objects (e.g.1210, 1220, 1230, 1242, and 1244) which can be perceived by the user viathe wearable system. The objects within the user's FOR 1200 may bevirtual and/or physical objects. For example, the user's FOR 1200 mayinclude physical object such as a chair, a sofa, a wall, etc. Thevirtual objects may include operating system objects such as e.g., arecycle bin for deleted files, a terminal for inputting commands, a filemanager for accessing files or directories, an icon, a menu, anapplication for audio or video streaming, a notification from anoperating system, text, a text editing application, a messagingapplication, and so on. The virtual objects may also include objects inan application such as e.g., avatars, virtual objects in games, graphicsor images, etc. Some virtual objects can be both an operating systemobject and an object in an application. In some embodiments, thewearable system can add virtual elements to the existing physicalobjects. For example, the wearable system may add a virtual menuassociated with a television in the room, where the virtual menu maygive the user the option to turn on or change the channels of thetelevision using the wearable system.

A virtual object may be a three-dimensional (3D), two-dimensional (2D),or one-dimensional (1D) object. For example, the virtual object may be a3D coffee mug (which may represent a virtual control for a physicalcoffee maker). The virtual object may also be a 2D graphicalrepresentation of a clock (displaying current time to the user). In someimplementations, one or more virtual objects may be displayed within (orassociated with) another virtual object. A virtual coffee mug may beshown inside of a user interface plane, although the virtual coffee mugappears to be 3D within this 2D planar virtual space.

The objects in the user's FOR can be part of a world map as describedwith reference to FIG. 9. Data associated with objects (e.g. location,semantic information, properties, etc.) can be stored in a variety ofdata structures such as, e.g., arrays, lists, trees, hashes, graphs, andso on. The index of each stored object, wherein applicable, may bedetermined, for example, by the location of the object. For example, thedata structure may index the objects by a single coordinate such as theobject's distance from a fiducial position (e.g., how far to the left orright of the fiducial position, how far from the top or bottom of thefiducial position, or how far depth-wise from the fiducial position).The fiducial position may be determined based on the user's position(such as the position of the user's head). The fiducial position mayalso be determined based on the position of a virtual or physical object(such as a target object) in the user's environment. Accordingly, the 3Dspace in the user's environment may be represented in a 2D userinterface where the virtual objects are arranged in accordance with theobject's distance from the fiducial position.

In FIG. 12B, the FOV 1250 is schematically illustrated by dashed line1252. The user of the wearable system can perceive multiple objects inthe FOV 1250, such as the object 1242, the object 1244, and a portion ofthe object 1230. As the user's pose changes (e.g., head pose or eyepose), the FOV 1250 will correspondingly change, and the objects withinthe FOV 1250 may also change. For example, the map 1210 is initiallyoutside the user's FOV in FIG. 12B. If the user looks toward the map1210, the map 1210 may move into the user's FOV 1250, and (for example),the object 1230 may move outside the user's FOV 1250.

The wearable system may keep track of the objects in the FOR 1200 aswell as the objects in the FOV 1250. For example, the local processing &data module 260 can communicate with the remote processing module 270and remote data repository 280 to retrieve virtual objects in the user'sFOR. The local processing & data module 260 can store the virtualobjects, for example, in a buffer or a temporary storage. The localprocessing & data module 260 can determine a user's FOV using thetechniques descried herein and render a subset of the virtual objectsthat are in the user's FOV. When the user's pose changes, the localprocessing & data module 260 can update the user's FOV and accordinglyrender another set of virtual objects corresponding to the user'scurrent FOV.

Overview of Various User Input Modes

A wearable system can be programmed to accept various modes of inputsfor performing an operation. For example, the wearable system can accepttwo or more of the following types of input modes: voice commands, headposes, body poses (which may be measured, e.g., by an IMU in a belt packor a sensor external to the HMD), eye gazes (also referred to herein aseye pose), hand gestures (or gestures by other body parts), signals froma user input device (e.g., a totem), environmental sensors, etc.Computing devices are typically engineered to generate a given outputbased on a single input from the user. For example, a user can input atext message by typing on a keyboard or guide a movement of a virtualobject using a mouse, which are examples of hand gesture input modes. Asanother example, the computing device can receive a stream of audio datafrom the user's voice and translate the audio data into an executablecommand using voice recognition techniques.

A user input mode may, in some cases, be non-exclusively classified as adirect user input or an indirect user input. The direct user input maybe a user interaction directly supplied by a user, e.g., via avolitional movement of the user's body (e.g., turning the head or eyes,staring at an object or location, saying a phrase, moving a finger orhand). As an example of a direct user input, the user can interact withthe virtual object using a pose such as, e.g., a head pose, an eye pose(also referred to as eye gaze), a hand gesture, or another body pose.For example, the user can look (with head and/or eyes) at a virtualobject. Another example of the direct user input is the user's voice.For example, a user can say “launch a browser” to cause the HMD to opena browser application. As yet another example of the direct user input,the user can actuate a user input device, e.g., via a touch gesture(such as touching a touch-sensitive portion of a totem) or a bodymovement (such as rotating a totem functioning as amulti-degree-of-freedom controller).

In addition or in alternative to direct user input, the user can alsointeract with a virtual object based on an indirect user input. Theindirect user input may be determined from various contextual factors,such as, e.g., a geolocation of the user or the virtual object, anenvironment of the user, etc. For example, the user's geolocation may bein the user's office (rather than the user's home) and different tasks(e.g., work related tasks) can be executed based on the geolocation(e.g., derived from a GPS sensor).

The contextual factor can also include an affordance of the virtualobject. The affordance of the virtual object can comprise a relationbetween the virtual object and the environment of the object whichaffords an opportunity for an action or use associated with the object.The affordances may be determined based on, for example, the function,the orientation, the type, the location, the shape, and/or the size ofthe object. The affordances may also be based on the environment inwhich the virtual object is located. As examples, an affordance of ahorizontal table is that objects can be set onto the table, and anaffordance of a vertical wall is that objects may be hung from orprojected onto the wall. As an example, the may say “place that there”and a virtual office calendar is placed so as to appear horizontal onthe user's desk in the user's office.

A single mode of direct user input may create a variety of limitations,where the number or the type of available user interface operations maybe restricted due to the type of user inputs. For example, the user maynot be able to zoom in or zoom out with head pose because the head posemay not be able to provide precise user interactions. As anotherexample, the user may need to move the thumb back and forth (or move thethumb over a large amount of distance) on a touchpad in order to move avirtual object from the floor to the wall, which may create user fatigueover time.

Some direct input modes, however, may be more convenient and intuitivefor a user to provide. For example, a user can talk to the wearablesystem to issue a voice command without needing to type up the sentenceusing gesture-based keyboard input. As another example, the user can usea hand gesture to point at a target virtual object, rather than moving acursor to identify the target virtual object. While they may not be asconvenient or intuitive, other direct input modes can increase accuracyof the user interaction. For example, a user can move a cursor to thevirtual object to indicate the virtual object is the target object.However, as described above, if a user wants to select the same virtualobject using a direct user input (e.g., a head pose, or other inputsthat are direct results of a user's action), the user may need tocontrol the precise movement of the head, which can cause musclefatigue. A 3D environment (e.g. a VR/AR/MR environment) can addadditional challenges to user interactions because user input will alsoneed to be specified with respect to the depth (as opposed to a planarsurface). This additional depth dimension can create more opportunitiesfor errors than a 2D environment. For example, in 2D environment, a userinput can be translated with respect to a horizontal axis and a verticalaxis in a coordinate system while the user input may need to betranslated with respect to 3 axes (horizontal, vertical, and depth) in a3D environment. Accordingly, an imprecise transaction of a user inputcan cause errors in 3 axes (rather than 2 axes in the 2D environment).

To utilize the existing benefits of direct user inputs while improvingaccuracy of interacting with objects in the 3D space and reducing userfatigue, multiple modes of direct inputs may be used to execute a userinterface operation. The multimodal inputs can further improve existingcomputing devices (in particular a wearable device) for interactionswith virtual objects in a data rich and dynamic environment, such as,e.g., an AR, VR, or MR environment.

In multimodal user input techniques, one or more of the direct inputsmay be used to identify a target virtual object (also referred to as asubject) which a user will interact with and to determine a userinterface operation that will be performed on the target virtual object.For example, the user interface operation may include a commandoperation, such as select, move, zoom, pause, play, and a parameter ofthe command operation (such as, e.g., how to carry out the operation,where or when to the operation will occur, with which object will thetarget object interact, etc.). As an example of identifying a targetvirtual object and determining an interaction to be performed on thetarget virtual object, a user may look at a virtual sticky note (a heador eye pose mode of input), point at a table (a gesture mode of input),and say “move that there” (a voice mode of input). The wearable systemcan identify that the target virtual object in the phrase “move thatthere” is the virtual sticky note (“that”) and can determine the userinterface operation involves moving (the executable command) the virtualsticky note to the table (“there”). In this example, the commandoperation can be to “move” the virtual object, while the parameter ofthe command operation can include a destination object, which is thetable that the user is pointing at. Advantageously, in certainembodiments, the wearable system can increase overall accuracy of a userinterface operation or can increase the convenience of a user'sinteraction by performing a user interface operation based on multiplemodes of direct user inputs (e.g., three modes in the above example,head/eye pose, gesture, and voice). For example, instead of saying “movethe leftmost browser 2.5 feet to the right”, the user can say “move thatthere” (without pointing out the object being moved in the speech input)while using head or hand gestures indicating the object is the leftmostbrowser and use head or hand movements to indicate the distance of themovement.

Examples Interactions in a Virtual Environment Using Various Input Modes

FIG. 13 illustrates examples of interacting with a virtual object usingone mode of user input. In FIG. 13, a user 1310 wears an HMD and isinteracting with virtual content in three scenes 1300 a, 1300 b, and1300 c. The user's head position (and corresponding eye gaze direction)is represented by a geometric cone 1312 a. In this example, the user canperceive the virtual content via the display 220 of HMD. Whileinteracting with the HMD, the user can input a text message via the userinput device 466. In the scene 1300 a, the user's head is at its naturalresting position 1312 a and the user's hands are also at their naturalresting position 1316 a. However, although the user may be morecomfortable typing in the text on the user input device 466, the usercannot see the interface on the user input device 466 to ensure that thecharacter is correctly typed.

In order to see the text entered on the user input device, the user canmove the hands up to position 1316 b as shown in the scene 1300 b.Accordingly, the hands will be in the FOV of the user's head when thehead is at its natural resting position 1312 a. However, the position1316 b is not a natural resting position of the hands, and it may causeuser fatigue as a result. Alternatively, as illustrated in the scene1300 c, the user can move her head to the position 1312 c in order tomaintain the hands at the natural resting position 1316 a. However, themuscles around the user's neck may become fatigued due to the unnaturalposition of the head and the user's FOV is pointed toward the ground orfloor rather than toward the outward world (which may be unsafe if theuser were walking in a crowded area). In either the scene 1300 b or thescene 1300 c, the user's natural ergonomics are sacrificed to meet adesired user interface operation when the user is performing the userinterface operation using a single input mode.

The wearable system described herein can at least partially alleviatethe ergonomic limitations depicted in the scenes 1300 b and 1300 c. Forexample, a virtual interface can be projected within the field of viewof the user in the scene 1300 a. The virtual interface can allow theuser to observe the typed input from a natural position.

The wearable system can also display and support interactions withvirtual content free from device constraints. For example, the wearablesystem can present multiple types of virtual content to a user and auser can interact with one type of content using a touchpad whileinteracting with another type of content using a keyboard.Advantageously, in some embodiments, the wearable system can determinewhich virtual content is a target virtual object (that the user isintended to act upon) by calculating a confidence score (with a higherconfidence score indicative of a higher confidence (or likelihood) thatthe system has identified the correct target virtual object). Detailedexamples on identifying the target virtual object are described withreference to FIGS. 15-18B.

FIG. 14 illustrates examples of selecting a virtual object using acombination of user input modes. In the scene 1400 a, the wearablesystem can present a user 1410 with a plurality of virtual objects,represented by a square 1422, a circle 1424, and a triangle 1426.

The user 1410 can interact with the virtual objects using head pose asillustrated in the scene 1400 b. This is an example of a head pose inputmode. The head pose input mode may involve a cone cast to target orselect virtual objects. For example, the wearable system can cast a cone1430 from a user's head toward the virtual objects. The wearable systemcan detect whether one or more of the virtual objects fall within thevolume of the cone to identify which object the user intends to select.In this example, the cone 1430 intersects with the circle 1424 and thetriangle 1426. Therefore, the wearable system can determine that theuser intends to select either the circle 1424 or the triangle 1426.However, because the cone 1430 intersects with both the circle 1424 andthe triangle 1426, the wearable system may not be able to ascertainwhether the target virtual object is the circle 1424 or the triangle1426 based on the head pose input alone.

In the scene 1400 c, the user 1410 can interact with the virtual objectsby manually orienting a user input device 466, such as totem (e.g., ahandheld remote control device). This is an example of a gesture inputmode. In this scene, the wearable system can determine that either thecircle 1424 or the square 1422 is the intended target because these twoobjects are in the direction at which the user input device 466 ispointing. In this example, the wearable system can determine thedirection of the user input device 466 by detecting a position ororientation of the user input device 466 (e.g., via an IMU in the userinput device 466), or by performing a cone cast originating from theuser input device 466. Because both the circle 1424 and the square 1422are candidates for the target virtual objet, the wearable system cannotascertain which one of them is the object that the user actually wantsto select based solely on the gesture input mode.

In the scene 1400 d, the wearable system can use multimodal user inputsto determine the target virtual object. For example, the wearable systemcan use both the results obtained from the cone cast (head pose inputmode) and from the orientation of the user input device (gesture inputmode) to identify the target virtual object. In this example, the circle1424 is the candidate identified in both the result from the cone castand the result obtained from the user input device. Therefore, thewearable system can determine with high confidence, using these twoinput modes, that the target virtual object is the circle 1424. Asfurther illustrated in the scene 1400 d, the user can give a voicecommand 1442 (illustrated as “Move that”), which is an example of athird input mode (namely, voice), to interact with the target virtualobject. The wearable system can associate the word “that” with thetarget virtual object, the word “Move” with the command to be executed,and can accordingly move the circle 1424. However, the voice command1442 by itself (without indications from the user input device 466 orthe cone cast 143) may cause confusion to the wearable system, becausethe wearable system may not know which object is associated with theword “that”.

Advantageously, in some embodiments, by accepting multiple modes ofinput to identify and interact with a virtual object, the amount ofprecision required for each mode of input may be reduced. For example,the cone cast may not be able to pinpoint an object at a rendering planethat is far away because the diameter of the cone increases as the conegets farther away from the user. As other examples, the user may need tohold the input device at a particular orientation to point toward atarget object and speak with a particular phrase or pace to ensure thecorrect voice input. However, by combining the voice input and theresults from the cone cast (either from a head pose or a gesture usingthe input device), the wearable system can still identify the targetvirtual object without requiring either input (e.g., the cone cast orthe voice input) to be precise. For example, even though the cone castselects multiple objects (e.g., as described with reference to scenes1400 b, 1400 c), the voice input may help narrow down the selection(e.g., increase the confidence score for the selection). For example,the cone cast may capture 3 objects, among which the first object is tothe user's right, the second object is to the user's left, and the thirdobject is in the center of the user's FOV. The user can narrow theselection by saying “select the rightmost object”. As another example,there may be two identically shaped objects in the user's FOV. In orderfor the user to select the correct object, the user may need to givemore descriptions to the object via voice command. For example, ratherthan saying “select the square object”, the user may need to say “selectthe square object that is red”. However, with cone cast, the voicecommand may not have to be as precise. For example, the user can look atone of the square object and say “select the square object” or even“select the object”. The wearable system can automatically select thesquare object that coincides with the user's gaze direction and will notselect the square object that is not in the user's gaze direction.

In some embodiments, the system may have a hierarchy of preferences forcombinations of input modes. For example, a user tends to look in thedirection his or her head is pointing; therefore, eye gaze and head posemay provide information that is similar to each other. A combination ofhead pose and eye gaze may be less preferred, because the combinationdoes not provide much extra information as compared to the use of eyegaze alone or head pose alone. Accordingly, the system may use thehierarchy of modal input preferences to select modal inputs that providecontrasting information rather than generally duplicative information.In some embodiments, the hierarchy is to use head pose and voice as theprimary modal inputs, followed by eye gaze and gesture.

Accordingly, as described further herein, based on multimodal inputs,the system can calculate a confidence score for various objects in theuser's environment that each such object is the target object. Thesystem can select, as the target object, the particular object in theenvironment that has the highest confidence score.

FIG. 15 illustrates an example of interacting with a virtual objectusing a combination of direct user inputs. As depicted in FIG. 15, auser 1510 wears an HMD 1502 configured to display virtual content. TheHMD 1502 may be part of the wearable system 200 described herein and mayinclude a belt-worn power & processing pack 1503. The HMD 1502 may beconfigured to accept user input from a totem 1516. The user 1510 of theHMD 1502 can have a first FOV 1514. The user can observe a virtualobject 1512 in the first FOV 1514.

The user 1510 can interact with the virtual object 1512 based on acombination of direct inputs. For example, the user 1510 can select thevirtual object 1512 through a cone casting technique based on the user'shead or eye pose or by a totem 1516, by a voice command, or by acombination of these (or other) input modes (e.g., as described withreference to FIG. 14).

The user 1510 may shift her head pose to move the selected virtualobject 1512. For example, the user can turn her head leftward to causethe FOV to be updated from the first FOV 1514 to the second FOV 1524 (asshown from the scene 1500 a to the scene 1500 b). The movement of theuser's head can be combined with other direct inputs to cause thevirtual object be moved from the first FOV 1514 to the second FOV 1524.For example, the change in the head pose can be aggregated with otherinputs such as, e.g., a voice command (“move that, to there”), guidancefrom the totem 1516, or an eye gaze direction (e.g., as recorded by theinward-facing imaging system 462 shown in FIG. 4). In this example, theHMD 1502 can use the updated FOV 1524 as a general region that thevirtual object 1512 should be moved into. The HMD 1502 can furtherdetermine the destination of the virtual object's 1512 movement based onthe user's direction of gaze. As another example, the HMD may capture avoice command “move that there”. The HMD can identify the virtual object1512 as the object that the user will interact on (because the user haspreviously selected the virtual object 1512). The HMD can furtherdetermine that the user intends to move the object from the FOV 1514 tothe FOV 1524 by detecting a change of the user's head pose. In thisexample, the virtual object 1512 may initially be in the central portionof the user's first FOV 1514. Based on the voice command and the user'shead pose, the HMD may move the virtual object to the center of theuser's second FOV 1524.

Examples of Identifying a Target Virtual Object or a User InterfaceOperation with Multimodal User Inputs

As described with reference to FIG. 14, in some situations, the wearablesystem may not be able to identify (with sufficient confidence) a targetvirtual object with which the user intends to interact using a singlemode of input. Further, even if multiple modes of user inputs are used,one mode of user input may indicate one virtual object while anothermode of user input may indicate a different virtual object.

To resolve ambiguities and to provide an improved wearable system whichsupports multimodal user inputs, the wearable system can aggregate themodes of user inputs and calculate a confidence score to identify adesired virtual object or user interface operation. As explained above,a higher confidence score indicates a higher probability or likelihoodthat the system has identified the desired target object.

FIG. 16 illustrates an example computing environment for aggregatinginput modes. The example environment 1600 includes three virtualobjects, e.g., associated with the applications A 1672, B 1674, and C1676. As described with reference to FIGS. 2A, 2B, and 9, a wearablesystem can include a variety of sensors and can receive a variety ofuser inputs from these sensors and analyze the user inputs to interactwith a mixed reality 960, for example, using the transmodal input fusiontechniques described herein. In the example environment 1600, a centralruntime server 1650 can aggregate direct inputs 1610 and indirect userinputs 1630 to produce a multimodal interaction for an application.Examples of direct inputs 1610 may include a gesture 1612, head pose1614, voice input 1618, totem 1622, direction of eye gaze (e.g., eyegaze tracking 1624), other types of direct inputs 1626, etc. Examples ofindirect input 1630 may include environment information (e.g.,environment tracking 1632), and geolocation 1634. The central runtimeserver 1650 may include the remote processing module 270. In certainimplementations the local processing and data module 260 (or theprocessor 128) may perform one or more functions of the central runtimeserver 1650. The local processing and data module 260 may alsocommunicate with the remote processing module 270 to aggregate inputmodes.

A wearable system can track the gesture 1612 using the outward-facingimaging system 464. The wearable system can use a variety of techniquesdescribed in FIG. 9 to track hand gestures. For example, theoutward-facing imaging system 464 can acquire images of the user'shands, and map the images to corresponding hand gestures. Theoutward-facing imaging system 464 may use the FOV camera or a depthcamera (configured for depth detection) to image a user's hand gesture.The central runtime server 1650 can use object recognizer 708 toidentify the user's head gesture. The gesture 1612 can also be trackedby the user input device 466. For example, the user input device 466 mayinclude a touch sensitive surface which can track the user's handmovements, such as, e.g., a swipe gesture or a tap gesture.

An HMD can recognize head poses 1614 using an IMU. A head 1410 may havemultiple degrees of freedom, including three types of rotations (e.g.yaw, pitch, and roll) and three types of translations (e.g., surging,swaying, and heaving). The IMU can be configured, for example, tomeasure 3-DOF movements or 6-DOF movements of the head. The measurementsobtained from the IMU may be communicated to the central runtime server1650 for processing (e.g., to identify a head pose).

The wearable system can use an inward-facing imaging system 462 toperform eye gaze tracking 1624. For example, the inward-facing imagingsystem 462 can include eye cameras configured to obtain images of theuser's eye region. The central runtime server 1650 can analyze theimages (e.g., via the object recognizers 708) to deduce the user'sdirection of gaze or to track the user's eye movements.

The wearable system can also receive inputs from the totem 1622. Asdescribed herein, the totem 1622 can be an embodiment of the user inputdevice 466. Additionally or alternatively, the wearable system canreceive voice input 1618 from a user. The inputs from the totem 1622 andthe voice input 1618 can be communicated to the central runtime server1650. The central runtime server 1650 can use natural languageprocessing in real-time or near real-time for parsing the user's audiodata (for example acquired from the microphone 232). The central runtimeserver 1650 can identify the content of the speech by applying variousspeech recognition algorithms, such as, e.g., hidden Markov models,dynamic time warping (DTW)-based speech recognitions, neural networks,deep learning algorithms such as deep feedforward and recurrent neuralnetworks, end-to-end automatic speech recognitions, machine learningalgorithms (described with reference to FIGS. 7 and 9), semanticanalysis, other algorithms that uses acoustic modeling or languagemodeling, etc. The central runtime server 1650 can also apply voicerecognition algorithms which can identify the identity of the speaker,such as whether the speaker is the user of the wearable device or aperson in the user's background.

The central runtime server 1650 can also receive indirect inputs when auser interacts with the HMD. The HMD can include various environmentalsensors described with reference to FIGS. 2A and 2B. Using data acquiredby the environmental sensors (along or in combination of data related tothe direct input 1610), the central runtime server 1650 can reconstructor update the user's environment (such as, e.g., the map 920). Forexample, the central runtime server 1650 can determine the user'sambient light condition based on the user's environment. This ambientlight condition may be used to determine which virtual object the usercan interact with. For example, when a user is in a bright environment,the central runtime server 1650 may identify the target virtual objectto be the virtual object that supports gestures 1612 as an input modebecause the cameras can observe the user's gestures 1612. However, ifthe environment is dark, the central runtime server 1650 may determinethat the virtual object may be an object that supports voice input 1618rather than gestures 1612.

The central runtime server 1650 can perform the environmental tracking1632 and aggregate direct input modes to produce multimodal interactionfor a plurality of applications. As an example, when a user enters intoa noisy environment from a quiet environment, the central runtime server1650 may disable the voice input 1618. Additional examples on selectingthe modes of inputs based on the environments are further described withreference to FIG. 24.

The central runtime server 1650 can also identify a target virtualobject based on geolocation information of the user. The geolocationinformation 1634 may also be acquired from an environmental sensor (suchas, e.g., a GPS sensor). The central runtime server 1650 may identify avirtual object for potential user interactions where the distancebetween the virtual object and the user is within a threshold distance.Advantageously, in some embodiments, a cone in a cone cast may have alength that is adjustable by the system (e.g., based on number ordensity of objects in the environment). By selecting objects within acertain radius of the user, the number of potential objects that may betarget objects can significantly be reduced. Additional examples ofusing indirect input as a mode of input are described with reference toFIG. 21.

Examples of Ascertaining a Target Object

The central runtime server 1650 can use a variety of techniques todetermine a target object. FIG. 17A illustrates an example ofidentifying a target object using a lattice tree analysis. The centralruntime server 1650 can derive a given value from an input source andproduce a lattice of possible values for candidate virtual objects thata user may potentially interact. In some embodiments, the value can be aconfidence score. A confidence score can include a ranking, a rating, avaluation, quantitative or qualitative values (e.g., a numerical valuein a range from 1 to 10, a percentage or percentile, or a qualitativevalue of “A”, “B”, “C”, and so on), etc. Each candidate object may beassociated with a confidence score, and in some cases, the candidateobject with the highest confidence score (e.g., higher than otherobject's confidence scores or higher than a threshold score) is selectedby the system as the target object. In other cases, objects withconfidence scores below a threshold confidence score are eliminated fromconsideration by the system as the target object, which can improvecomputational efficiency.

In many of the examples herein, a reference is made to selection of atarget virtual object or selection from a group of virtual objects. Thisis intended to illustrate example implementations but is not intended tobe limiting. The techniques described can be applied to virtual objectsor physical objects in the user's environment. For example, the voicecommand “move that there” may be in reference to moving a virtual object(e.g., a virtual calendar) onto a physical object (e.g., the horizontalsurface of the user's desk). Or the voice command “move that there” maybe in reference to moving a virtual object (e.g., a virtual wordprocessing application) to another location within another virtualobject (e.g., another position in the user's virtual desktop).

The context of the command may also provide information as to whetherthe system should attempt to identify virtual objects, physical objects,or both. For example, in the command “move that there”, the system canrecognize that “that” is a virtual object, because the AR/VR/MR systemcannot move an actual, physical object. Accordingly, the system mayeliminate physical objects as candidates for “that”. As described in theexamples above, the target location “there” might be a virtual object(e.g., the user's virtual desktop) or a physical object (e.g., theuser's desk).

Also, the system may assign confidence scores to objects in the user'senvironment, which may be the FOR, FOV, or field of fixation (see, e.g.,FIG. 12A), depending on the context and the goals of the system at thatpoint in time. For example, a user may wish to move a virtual calendarto a position on the user's desk, both of which are in the FOV of theuser. The system may analyze objects within the user's FOV, rather thanall objects in the user's FOR, because the context of the situationsuggests that the command to move the virtual calendar will be to atarget destination in the user's FOV, which may improve processing speedor efficiency. In another case, the user may be reviewing a menu ofmovie selections in a virtual movie application and may be fixating on asmall selection of movies. The system may analyze (and, e.g., provideconfidence scores) for just the movie selections in the user's field offixation (based, e.g., on the user's eye gaze), rather than the full FOV(or FOR), which also may increases processing efficiency or speed.

With reference to the example shown in FIG. 17A, a user can interactwith a virtual environment using two input modes, head pose 1614 and eyegaze 1624. Based on the head pose 1614, the central runtime server 1650can identify two candidate virtual objects associated with application A1672 and application B 1674. The central runtime server 1650 can evenlydistribute a confidence score of 100% between the application A 1672 andthe application B 1674. As a result, the application A 1672 and theapplication B 1674 may each be assigned a confidence score 50%. Thecentral runtime server 1650 can also identify two candidate virtualobjects (application A 1672 and application C 1676) based on thedirection of eye gaze 1624. The central runtime server 1650 can alsodivide a 100% confidence between the application A 1672 and theapplication C 1676.

The central runtime server 1650 may perform a lattice compression logicfunction 1712 to reduce or eliminate outlier confidence values that arenot common among the multiple input modes, or those confidence valuesthat fall below a certain threshold to determine the most likelyapplication that a user intends to interact with. For example, in FIG.17A, the central runtime server 1650 can eliminate application B 1674and application C 1676 because these two virtual objects are notidentified by both the head pose 1614 and the eye gaze 1624 analysis. Asanother example, the central runtime server 1650 can aggregate thevalues assigned to each application. The central runtime server 1650 canset a threshold confidence value to be equal to or above 80%. In thisexample, application A's 1672 aggregated value is 100% (50%+50%);application B's 1674 aggregated value is 50%; and the application C's1676 value is 50%. Because the individual confidence values forapplications B and C are below the threshold confidence value, thecentral runtime server 1650 may be programmed not to select applicationsB and C, but rather to select the application A 1672, becauseapplication A's aggregated confidence value (100%) is greater than thethreshold confidence value.

Although the example in FIG. 17A divides the value (e.g., the confidencescore) associated with an input device equally among candidate virtualobjects, in certain embodiments, the value distribution may not be equalamong candidate virtual objects. For example, if the head pose 1614 hasa value of 10, application A 1672 may receive a value of 7 andapplication B 1674 may receive a value of 3 (because the head pose ispointing more towards A 1672). As another example, if the head pose 1614has a qualitative grade “A”, the application A 1672 may be assignedgrade “A” while application B 1674 and C 1676 do not receive anythingfrom the head pose 1614.

The wearable system (e.g., the central runtime server 1650) can assign afocus indicator to the target virtual object so that the user can morereadily perceive the target virtual object. The focus indicator can be avisual focus indicator. For example, the focus indicator can comprise ahalo (substantially surrounding or near the object), a color, aperceived size or depth change (e.g., causing the target object toappear closer and/or larger when selected), or other visual effectswhich draw the user's attention. The focus indicator can also includeaudible or tactile effects such as vibrations, ring tones, beeps, etc.The focus indicator can provide useful feedback to the user that thesystem is “doing the right thing” by confirming to the user (via thefocus indicator) that the system has correctly determined the objectsassociated with the command (e.g., correctly determined “that” and“there” in a “move that there” command). For example, the identifiedtarget virtual object can be assigned a first focus indicator and thedestination location (e.g., “there” in the command) can be assigned asecond focus indicator. In some cases, if the system has incorrectlydetermined the target object(s), the user may override the system'sdetermination, e.g., by staring (fixating) at the correct object andproviding a voice command such as “no, this not that”.

Examples of Identifying a Target User Interface Operation

In addition to or in alternative to identifying a target virtual object,the central runtime server 1650 can also determine a target userinterface operation based on multiple inputs received. FIG. 17Billustrates an example of determining a target user interface operationbased on multimodal inputs. As depicted, the central runtime server 1650can receive multiple inputs in the form of a head pose 1614 and agesture 1612. The central runtime server 1650 can display multiplevirtual objects associated with, e.g., application A 1672 andapplication B 1674, to a user. The head pose input mode by itself,however, may be insufficient to determine the desired user interfaceactions because there is a 50% confidence that the head pose applies toa user interface operation (shown as modification options 1772)associated with the application A 1672 and there is another 50%confidence that the head pose applies to another user interfaceoperation (shown as modification options 1774) associated with theapplication B 1674.

In various embodiments, a particular application or a type of userinterface operations may be programmed to be more responsive to acertain mode of input. For example, the HTML tags or JavaScriptprogramming of the application B 1674 may be set to be more responsiveto a gesture input more than that of the application A 1672. Forexample, the application A 1672 may be more responsive to a head pose1672 than a gesture 1612, while a “select” operation may be moreresponsive to the gesture 1612 (e.g., a tap gesture) than the head pose1614, because a user may be more likely to use a gesture to select anobject than a head pose in some cases.

With reference to FIG. 17B, the gesture 1612 may be more responsive to acertain type of user interface operation in the application B 1674. Asillustrated, the gesture 1612 may have a higher confidence associatedwith user interface operations for application B while the gesture 1612may not be applicable for interface operations in the application A1672. Accordingly, if the target virtual object is the application A1672, the input received from the head pose 1614 may be the target userinterface operation. But if the target virtual object is the applicationB 1674, then the input received from the gesture 1612 (alone or incombination with the input based on the head pose 1614) may be thetarget user interface operation.

As another example, because the gesture 1612 has a higher confidencelevel than the head pose 1614 when the user is interacting with theapplication B, the gesture 1612 may become the primary input mode forapplication B 1674 while the head pose 1614 may be the secondary inputmode. Accordingly, the input received from the gesture 1612 may beassociated with a higher weight than the head pose 1614. For example, ifthe head pose indicates that a virtual object associated with theapplication B 1674 should stay still while the gesture 1612 indicatesthat the virtual object should be moved leftward, the central runtimeserver 1650 may render the virtual object moving leftward. In certainimplementations, a wearable system can allow a user to interact with avirtual object using the primary input mode and can consider thesecondary input mode if the primary input mode is insufficient todetermine the user's action. For example, the user can interact with theapplication B 1674 with mostly gestures 1612. However, when the HMDcannot determine a target user interface operation (because e.g., theremay be multiple candidate virtual objects in the application B 1674 orif the gesture 1612 is unclear), the HMD can use head pose as an inputto ascertain the target virtual object or a target user interfaceoperation to be performed on the application B 1674.

The score associated with each input mode may be aggregated to determinea desired user interface operation. FIG. 17C illustrates an example ofaggregating confidence scores associated with input modes for a virtualobject. As illustrated in this example, a head pose input 1614 producesa higher confidence score for application A (80% confidence) overapplication B (30% confidence), whereas the gesture input 1612 producesa higher confidence score for application B (60% confidence) overapplication A (30% confidence). The central runtime server 1650 canaggregate the confidence scores for each objects based on the confidencescores derived from each user input mode. For example, the centralruntime server 1650 can produce an aggregate score of 110 forapplication A 1672 and an aggregate score of 90 for application B 1674.The aggregated scores may be weighted or unweighted averages or othermathematical combinations. Because the application A 1672 has a higheraggregate score than Application B 1674, the central runtime server 1650may select application A as the application to be interacted with.Additionally or alternatively, due to the higher aggregation score ofthe application A 1672, the central runtime server 1650 can determinethat the head pose 1614 and the gesture 1612 are intended to perform anuser interface operation on the application A 1672, even though theapplication B is more “responsive” to the gesture 1612 than applicationA.

In this example, the central runtime server 1650 aggregates theconfidence scores occurred by adding the confidence scores of variousinputs for a given object. In various other embodiments, the centralruntime server 1650 can aggregate the confidence scores using techniquesother than a simple addition. For example, an input mode or a score maybe associated with a weight. As a result, the aggregation of confidencescores will take into account the weight assigned to the input mode orthe score. The weights may be user adjustable to permit the user toselectively adjust the “responsiveness” of the multimodal interactionwith the HMD. The weights may also be contextual. For example, weightsused in a public place may emphasize head or eye pose over handgestures, to avoid possible social awkwardness of having the userfrequently gesture while operating the HMD. As another example, in asubway, airplane, or train, voice commands may be given less weight thanhead or eye poses, since a user may not wish to speak out loud to his orher HMD in such an environment. Environmental sensors (e.g., GPS) mayassist in determining the appropriate context for where the user isoperating the HMD.

Although the examples in FIGS. 17A-17C are illustrated with reference totwo objects, the techniques described herein can also be applied whenthere are more or fewer objects. In addition, techniques described withreference to these figures can be applied to applications of a wearablesystem or virtual objects associated with one or more applications.Furthermore, the techniques described herein can also be applied todirect or indirect input modes, other than head pose, eye gaze, orgestures. For example, the voice command may also be used. In addition,despite the central runtime server 1650 having been used as an examplethroughout to describe the processing of the various input modes, thelocal processing & data module 260 of the HMD may also perform a portionor all of the operations in addition to or in alterative to the centralruntime server 1650.

Example Techniques for Calculating a Confidence Score

The wearable system can use one or a combination of a variety oftechniques to calculate a confidence score of an object. FIGS. 18A and18B illustrate examples of calculating confidence scores for objectswithin a user's FOV. The user's FOV may be calculated based on theuser's head pose or eye gaze, for example, during a cone cast. Theconfidence scores in the FIGS. 18A and 18B may be based on a singleinput mode (such as e.g., the user's head pose). Multiple confidencescores can be calculated (for some or all of the various multimodalinputs) and then aggregated to determine a user interface operation or atarget virtual object based on multimodal user inputs.

FIG. 18A illustrates an example where the confidence score of a virtualobject is calculated based on the portion of the virtual object thatfalls within the user's FOV 1810. In FIG. 18A, the user's FOV has aportion of two virtual objects (represented by a circle 1802 and atriangle 1804). The wearable system can assign confidence scores to thecircle and the triangle based on the proportion of the projected area ofthe object that falls within the FOV 1810. As illustrated, approximatelyhalf of the circle 1802 falls within the FOV 1810, and as a result, thewearable system may assign a confidence score of 50% to the circle 1802.As another example, about 75% of the triangle is within the FOV 1810,Therefore, the wearable system may assign a confidence score of 75% tothe triangle 1804.

The wearable system can use regression analysis of content in the FOVand FOR to calculate the proportion of a virtual object within a FOV. Asdescribed with reference to FIG. 12B, although the wearable system keepstrack of the objects in the FOR, the wearable system may deliver theobjects (or portions of the objects) that are in the FOV to a renderingprojector (e.g., the display 220) for display within the FOV. Thewearable system can determine which portions are provided for therendering projector and analyze the proportion that is delivered to therendering projector against the virtual object as a whole to determinethe percentage of the virtual object that is within the FOV.

In addition to or as an alternative to calculating a confidence scorebased on the proportional area that falls within the FOV, the wearablesystem can also analyze the space near the object in the FOV todetermine a confidence score of the object. FIG. 18B illustrates anexample of calculating a confidence score based on the evenness of spacesurrounding a virtual object in the FOV 1820. The FOV 1820 includes twovirtual objects as depicted by the triangle 1814 and the circle 1812.The space around each virtual object may be represented by vectors. Forexample, the space around the virtual object 1812 may be represented byvectors 1822 a, 1822 b, 1822 c, and 1822 d, while the space around thevirtual object 1814 may be represented by vectors 1824 a, 1824 b, 1824c, and 1824 d. The vectors may originate from a virtual object (or aboundary to the virtual object) and end at the edge of the FOV 1820. Thesystem can analyze the distribution of the lengths of the vectors fromthe objects to the edge of the FOV to determine which of the objects ispositioned more towards the center of the FOV. For example, an object atthe very center of a circular FOV would have a relatively uniformdistribution of vector lengths, whereas an object very close to an edgewould have a non-uniform distribution of vector lengths (since somevectors pointing to the nearby edge would be shorter but vectorspointing to the most distant edge would be longer). As depicted in FIG.18B, the distribution of lengths of the vectors from the virtualtriangle 1814 to the edges of field of view 1820 vary more than thedistribution of lengths of the vectors from circle 1812 to the edges offield of view 1820, which indicates the virtual circle 1812 is moretowards the center of the FOV 1820 than the virtual triangle 1814. Thevariability of the distribution of the vector lengths may be representedby a standard deviation or variance (or other statistical measure) ofthe lengths. The wearable system can accordingly assign a higherconfidence score to the virtual circle 1812 over the virtual triangle1814.

Besides the techniques described with reference to FIGS. 18A and 18B,the wearable system can assign confidence score to a virtual objectbased on historical analysis of the user's interactions. As an example,the wearable system can assign a higher confidence score to a virtualobject with which the user frequently interacts. As another example, oneuser may tend to move virtual objects using voice commands (e.g., “movethat there”), whereas another user may prefer to use hand gestures(e.g., by reaching out and “grabbing” a virtual object and moving it toanother position). The system can determine such user tendencies fromthe historical analysis. As yet another example, an input mode may befrequently associated with a particular user interface operation or aparticular virtual object, as a result, the wearable system may increasethe confidence score to the particular user interface operation or theparticular virtual object, even though there may be an alternative userinterface operation or virtual object based on the same input.

Given either field of view 1810 or 1820 as depicted in FIG. 18A or 18B,a second input mode can facilitate the selection of the appropriatevirtual object or an appropriate user interface operation in the virtualobject. For example, a user can say “enlarge the triangle” to increasethe size of the triangle within field of view 1810. As another example,in FIG. 18A, a user may give a voice command, such as “make that twiceas big”. The wearable system may determine that the subject (e.g., thetarget object) of the voice command is the virtual object 1804 becausethe virtual object 1804 has a higher confidence score based on the headpose. Advantageously, in some embodiments this reduces the specificityof interaction needed to produce the desired result. For example, theuser don't have to say “make the triangle twice as big” in order for thewearable system to achieve the same interaction.

The triangles and circles in FIGS. 18A and 18B are for illustrationpurposes only. Various techniques described herein can also be appliedto virtual content that supports more complex user interactions.

Example Multimodal Interactions in a Physical Environment

In addition to or in alternative to interacting with virtual objects,the wearable system can also offer a broad range of interactions withina real world environment. FIGS. 19A and 19B illustrate examples ofinteracting with a physical environment using multimodal inputs. In FIG.19A, 3 modes of inputs are illustrated: hand gestures 1960, head pose1920, and inputs from the user input device 1940. The head pose 1920 canbe determined using pose sensors. The pose sensors may be an IMU,gyroscopes, magnetometers, accelerometers, or other types of sensorsdescribed with reference to FIGS. 2A and 2B. The hand gesture 1960 maybe measured using an outward-facing imaging system 464 while the userinput device 1940 may be an embodiment of the user input device 466shown in FIG. 4.

In some embodiments, the wearable system can also measure the user's eyegaze. The eye gaze may include a vector extending from each of theuser's eyes to a position where the two eyes' lines of sight converge.The vector can be used to determine the direction a user is looking andcan be used to select or identify virtual content at the convergencepoint or along the vector. Such eye gaze may be determined byeye-tracking techniques such as, e.g., glint detection, iris or pupilshape mapping, infrared illumination, or binocular eye imaging withregression of an intersection point originating from a respective pupilorientation. Eye gaze or head pose may then be considered a source pointfor a cone cast or ray cast for virtual object selection.

As described herein, an interaction event to move selected virtualcontent within a user's environment (for example, “put that there”) mayrequire determination of a command operation (e.g., “put”), a subject(e.g., “that” as may be determined from the above multimodal selectiontechniques), and a parameter (e.g., “there”). The command operation (orcommand for short) and the subject (which is also referred to as thetarget object or the target virtual object) may be determined using acombination of input modes. For example, a command to move the subject1912 may be based on a head pose 1920 change (e.g., head turning ornodding) or a hand gesture 1960 (e.g. a swipe gesture), alone or incombination. As another example, the subject 1912, may be determinedbased on a combination of head pose and eye gaze. Accordingly, thecommand based on multimodal user inputs can also sometimes be referredto as a multimodal input command.

The parameter may also be determined using a single input or amultimodal input. The parameter may be associated with objects in theuser's physical environment (e.g., a table or a wall) or objects in theuser's virtual environment (e.g., a movie application, an avatar or avirtual building in a game). Identifying a real world parameter canallow for a quicker and more accurate content placement response in someembodiments. For example, a particular virtual object (or a portion ofthe virtual object) may be substantially planar with a horizontalorientation (e.g., the normal of the virtual object is perpendicular toa floor of a room). When a user initiates an interaction of moving thevirtual object, the wearable system can identify a real world surfacewith a similar orientation (e.g., a tabletop) and move the virtualobject to the real world surface. In certain embodiments, such movementsmay be automatic. For example, the user may want to move a virtual bookfrom where it is sitting on a floor. The only available horizontalsurface in the room may be the user's study desk. Accordingly, thewearable system can automatically move the virtual book to the surfaceof the study desk in response to a voice command of “move that” withoutthe user inputting additional commands or parameters, because thesurface of the desk is the most likely location for where the user wouldwant to move the book. As another example, the wearable system canidentify real world surfaces of a suitable size for given content andthereby may provide better parameter matching for a user. For example,if a user is watching a virtual video screen with a given display sizeand desires to move it to a particular surface with a simple voicecommand, the system may determine which real world surfaces provide thenecessary surface area to best support the virtual video's display size.

The wearable system can identify a target parameter (e.g., a targetsurface) using the techniques described with reference to identifying atarget virtual object. For example, the wearable system can calculate aconfidence score associated with a plurality of target parameters basedon indirect user inputs or direct user inputs. As an example, thewearable system can calculate a confidence score associated with a wallbased on direct input (such as the user's head pose) and indirect input(such as the characteristics of the wall (e.g., a vertical surface)).

Example Techniques of Identifying Real World Parameters

The wearable system can use a variety of techniques to determine aparameter (such as a target location) of a multimodal input command. Forexample, the wearable system can use various depth sensing techniques,such as, e.g., applying the SLAM protocol to environmental depthinformation (e.g., described with reference to FIG. 9), or constructionor access of a mesh model of the environment. In some embodiments, depthsensing determines the distance between known points in a 3D space(e.g., the distance between sensors on an HMD) and a point of interest(“POI”) on a surface of an object in the real world (e.g., a wall forlocating virtual content). This depth information may be stored in theworld map 920. A parameter for the interaction may be determined based acollection of POIs.

The wearable system can apply these depth sensing techniques to dataobtained from depth sensors to determine the metes and bounds of aphysical environment. The depth sensors may be part of theoutward-facing imaging system 464. In some embodiments, depth sensorsare coupled to IMUs. The data acquired from the depth sensors can beused to determine orientation of a plurality of POIs relative to oneanother. For example, the wearable system can compute a truncated signeddistance function (“TSDF”) for the POIs. A TSDF can include a numericalvalue for each POI. The numerical value may be zero when a point iswithin a given tolerance of a particular plane, positive when a point isspaced away from the particular plane in a first direction (e.g., aboveor outside), and negative when the point is spaced away from theparticular plane in a second (e.g., opposite) direction (e.g., below orinside). The computed TSDF can be used to define a 3-D volumetric gridof bricks or boxes along orientations as determined by the IMU, whichare aligned in, above, and below the particular plane to construct orrepresenting a particular surface.

POIs outside of a given planar tolerance (e.g., with absolute value ofTSDF greater than the tolerance) may be eliminated, leaving only aplurality of POIs adjacent to one another within given tolerance, tocreate virtual representations of surfaces within the real worldenvironment. For example, the real world environment may include aconference table. There may be various other objects (e.g., telephones,laptop computers, coffee mugs, etc.) on top of the conference table. Forthe surfaces of the conference table, the wearable system can keep POIsassociated with the conference table and remove the POIs for the otherobjects. As a result, a planar map (delineating the surfaces of theconference table) can represent the conference table with only thepoints that belong to the conference table. The map can leave out thepoints associated with the objects on top of the conference table. Incertain embodiments, the collection of POIs remaining in the planar mapmay be referred to as “workable surfaces” of the environment, becausethese regions of the planar map represent space(s) where virtual objectsmay be placed. For example, when a user wants to move a virtual screento a table, the wearable system can identify suitable surfaces (such astable tops, walls, etc.) in the user's environment while eliminating theobjects (e.g., a coffee mug or a pencil or a wall painting) or surfaces(e.g., a surface of a bookshelf) that are not suited for placing thescreen. In this example, the identified suitable surfaces may be theworkable surfaces of the environment.

Referring back to the example shown in FIG. 19A, the environment 1900can include a physical wall 1950. An HMD or the user input device 1940can house a depth sensor system (such as, e.g., a time of flight sensoror vertical cavity surface emitting laser (VCSEL)) and pose sensors(such as, e.g., IMUs). The data obtained by the depth sensor system canbe used to identify various POIs in the user's environment. The wearablesystem can group POIs that are substantially planar together to form aboundary polygon 1910. The boundary polygon 1910 may be an exampleembodiment of a workable surface.

In some embodiments, the outward-facing imaging system 464 can identifya user gesture 1960 which may include a finger pointing to a regionwithin the real world environment 1900. The outward-facing imagingsystem 464 can identify a pre-measured boundary polygon 1910 bydetermining a sparse point vector construction of the finger pointingtowards boundary polygon 1910.

As illustrated in FIG. 19A, there can be a virtual video screen 1930inside of the boundary polygon 1910. The user can interact within thevirtual object 1912 inside of the virtual video screen 1930 usingmultimodal input. FIG. 19B depicts an interaction using multimodal inputof virtual content in a real world environment. The environment in FIG.19B includes a vertical surface 1915 (which may be part of a wall) and asurface 1917 on a table top. In a first state 1970 a, the virtualcontent 1926 is initially displayed within the boundary polygon 1972 aon the wall surface 1915. The user can select the virtual object 1926,for example, through a cone cast or a multimodal input (including two ormore of the gesture 1960, head pose 1920, eye gaze, or an input from theuser input device 1940).

The user can use another input as part of the multimodal input to selectthe surface 1917 as a destination. For example, the user can use a headpose combined with a hand gesture to indicate that the surface 1917 isthe destination. The wearable system can recognize the surface 1917 (andthe polygon 1972 b) by grouping POIs that appear to be on the sameplane. The wearable system can also use other surface recognitiontechniques to identify the surface 1917.

The user can also use a multimodal input to transfer the virtual content1126 to boundary polygon 1972 b on the surface 1917 as illustrated inthe second state 1970 b. For example, the user can move the virtualcontent 1926 through a combination of changes in head pose and amovement of the user input device 1940.

As another example, the user could say “move that there” via themicrophone 232 of the wearable system which can receive the audio streamand parse this command from it (as described herein). The user cancombine this voice command with a head pose, eye gaze, gesture, or anactuation of the totem. The wearable system can detect the virtualobject 1926 as the subject of this command because the virtual object1926 is the highest confidence object (see, e.g., the dashed lines inscene 1970 a indicating the user's finger 1960, HMD 1920 and totem 1940are oriented toward the object 1926). The wearable system can alsoidentify the command operation as “move” and determine the parameter ofthe command to be “there”. The wearable system can further determinethat “there” refers to boundary polygon 1972 b based on input modesother than the voice (e.g., eye gaze, head pose, gesture, totem).

A command in an interaction event can involve adjustments andcalculations of multiple parameters. For example, the parameters mayinclude a destination, a placement, an orientation, an appearance (e.g.,size or shape), or an animation of a virtual object. The wearable systemcan automatically calculate a parameter even though the direct input isnot explicit in changing the parameter. As an example, the wearablesystem can automatically change the orientation of the virtual object1926 when it is moved from a vertical surface 1915 to a horizontalsurface 1917. In the first state 1970 a, the virtual content 1926 is asubstantially vertical orientation on the surface 1915. When the virtualcontent 1926 is moved to the surface 1917 in the second state 1970 b,the orientation of the virtual content 1926 may be kept consistent(e.g., maintaining the vertical orientation) as shown by the virtualobject 1924. The wearable system can also automatically adjust theorientation of the virtual content 1926 to align with the orientation ofthe surface 1917 such that the virtual content 1926 appears to be in ahorizontal position as illustrated by the virtual object 1922. In thisexample, the orientation may be automatically adjusted based onenvironment tracking 1632 as an indirect input. The wearable system canautomatically consider the object's (e.g., the surface 1917)characteristics when the wearable system determines that the object isthe target destination object. The wearable system can adjust theparameters of the virtual object based on the characteristics of thetarget destination object. In this example, the wearable systemautomatically rotated the orientation of the virtual object 1926 basedon the orientation of the surface 1917.

Additional examples of automatically placing or moving virtual objectsare described in U.S. application Ser. No. 15/673,135, filed Aug. 9,2017, titled “AUTOMATIC PLACEMENT OF A VIRTUAL OBJECT IN ATHREE-DIMENSIONAL SPACE,” published as U.S. Pat. Pub. No. 2018\0045963,the disclosure of which is hereby incorporated by reference herein inits entirety.

In certain implementations, an input may explicitly modify multipleparameters. A voice command of “place that there flat” may alter theorientation of the virtual object 1926 in addition to identifying thesurface 1917 as the destination. In this example, both the word “flat”and the word “there” can be parameter values, where “there” causes thewearable system to update the location of the target virtual objectwhereas the word “flat” is associated with the orientation of the targetvirtual object at the destination location. To execute the parameter“flat”, the wearable system can match the orientation of the virtualobject 1926 to match the orientation of the surface 1917.

In addition to or as an alternative to selecting and moving a virtualobject, a multimodal input can interact with virtual content in otherways. FIG. 20 illustrates an example of automatically resizing a virtualobject based on multimodal inputs. In FIG. 20, the user 1510 can wear anHMD 1502 and can interact with virtual objects using hand gestures andvoice commands 2024. FIG. 20 illustrates four scenes 2000 a, 2000 b,2000 c, and 2000 d. Each scene includes a display screen and a virtualobject (illustrated by the smiley face).

In the scene 2000 a, the display screen has a size 2010 and the virtualobject has a size 2030. The user can change the hand gesture from thegesture 2020 to the gesture 2022 to indicate that the user wants toadjust the size of either the virtual object or the display screen. Theuser can use voice input 2024 to indicate whether the virtual object orthe display screen is the subject of manipulation.

As an example, the user may want to enlarge both the display screen andthe virtual object. Accordingly, the user can use the input gesture 2022as a command to enlarge. The parameter for the degree of expansion maybe expressed by the extent of the outstretched figures. In the meantime,the user can use the voice input 2024 to dictate the subject of theinteraction. As shown in the scene 2000 b, the user may say “all” toproduce an enlarged display 2012 and an enlarged virtual object 2032. Asanother example, in the scene 2000 c, the user may say “content” toproduce an enlarged virtual object 2034 while the size the displayscreen remains the same as that in the scene 2000 a. As yet anotherexample, in the scene 2000 d, the user can say “display” to produce anenlarged display screen 2016, while the virtual object remains the samesize as that in the scene 2000 a.

Examples of Indirect Input as an Input Mode

As described herein, a wearable system can be programmed to allow userinteractions with direct user inputs and indirect user inputs as part ofthe multimodal inputs. The direct user inputs may include head pose, eyegaze, voice input, gesture, inputs from a user input device, or otherinputs that directly from a user. Indirect inputs may include variousenvironment factors, such as, e.g., user's position, user'scharacteristics/preferences, object's characteristics, characteristicsof the user's environment, etc.

As described with reference to FIGS. 2A and 2B, the wearable system caninclude a location sensor, such as, e.g., a GPS or radar or lidar. Thewearable system can determine a subject of user's interactions as afunction of the object's proximity to the user. FIG. 21 illustrates anexample of identifying a target virtual object based on objects'locations. FIG. 21 schematically illustrates a bird's eye view 2100 ofthe user's FOR. The FOR can include a plurality of virtual objects 2110a-2110 q. The user can wear an HMD which can include a location sensor.The wearable system can determine candidate target objects based on theobjects' proximity to the user. For example, the wearable system canselect virtual objects within a threshold radius (e.g., 1 m, 2 m, 3 m, 5m, 10 m, or more) from the user as candidate target virtual objects. InFIG. 21, the virtual objects (e.g., virtual objects 21100, 2110 p, 2110q) fall within the threshold radius (illustrated by the dashed circle2122) from the user's position 2120. As a result, the wearable systemcan set virtual objects 2110 o-2110 q as candidate target virtualobjects. The wearable system can further refine the selections based onother inputs (such as e.g., the user's head pose). The threshold radiuscan depend on contextual factors such as the location of the user. Forexample, the threshold radius may be shorter if the user is in his orher office than if the user is outside in a park. The candidate objectscan be selected from a portion of the region 2122 within the thresholdradius from the user. For example, only those objects that are bothwithin the circle 2122 and in the user's FOV (e.g., generally in frontof the user) may be candidates, while objects within the circle 2122 butoutside the user's FOV (e.g., behind the user) may not be candidates. Asanother example multiple virtual objects may be along a common line ofsight. For example, a cone cast may select multiple virtual objects. Thewearable system can use the user's position as another input todetermine a target virtual object or a parameter for user interaction.For example, cone cast may select objects corresponding to differentdepth planes, but the wearable system may be configured to identify atarget virtual object as an object within the user's hand's reach.

Similar to direct input, an indirect input may also be assigned to avalue which can be used for calculating the confidence scores of avirtual object. For example, while multiple subjects or parameters werewithin a common confidence of selection, the indirect input couldfurther be used as a confidence factor. With reference to FIG. 21, thevirtual objects within the circle 2122 may have a higher confidencescore than the virtual objects in-between the circle 2122 and the circle2124 because the objects that are closer to the user's position 2120 aremore likely to be the objects that the user is interested in interactingwith.

In the example shown in FIG. 21, dashed circles 2122, 2124 areillustrated for convenience, representing the projection of a sphere ofcorresponding radius onto the plane shown in FIG. 21. This is forillustration and is not limiting; in other implementations, other shapedregions (e.g., polyhedral) may be chosen.

FIGS. 22A and 22B illustrate another example of interacting with auser's environment based on a combination of direct and indirect inputs.These two figures show two virtual objects, virtual object A 2212 andvirtual object B 2214 in the FOV 1270 of a world camera which may belarger than the FOV 1250 of the user. The virtual object A 2212 is alsowithin the FOV 1250 of the user. For example, the virtual object A 2212may be a virtual document that the user is currently viewing while thevirtual object B 2214 may be a virtual sticky note on a wall. However,while the user is interacting with virtual object A 2212, the user maywant to look at the virtual object B 2214 to obtain additionalinformation from the virtual object B 2214. As a result, the user mayturn the head rightward (to change the FOV 1250) in order to view thevirtual object B 2214. Advantageously, in some embodiments, rather thanturning the head, the wearable system may detect a change in the user'sdirection of gaze (toward the direction of the virtual object B 2214),and as a result, the wearable system can automatically move the virtualobject B 2214 within the user's FOV without needing the user to changehis head pose. The virtual object B may overlay the virtual object A (orbe included within the object A) or the object B may be placed withinthe user FOV 1250 but spaced at least partially apart from object A (sothat object A is also at least partly visible to the user).

As another example, the virtual object B 2214 may be on another userinterface screen. The user may want to switch in-between the userinterface screen having the virtual object A 2212 and the user interfacescreen having the virtual object B 2214. The wearable system can makethe switch without changing the user's FOV 1250. For example, upondetection of a change in eye gaze or an actuation of the user inputdevice, the wearable system can automatically move the user interfacescreen having the virtual object A 2212 to be outside of the user's FOV1250 while move the user interface screen having the virtual object B2214 to be inside of the user's FOV 1250. As another example, thewearable system can automatically overlay the user interface screenhaving the virtual object B 2214 on top of the user interface screenhaving the virtual object A 2212. Once the user provides an indicationthat he has finished with a virtual user interface screen, the wearablesystem can automatically move the virtual user interface screen outsideof the FOV 1250.

Advantageously, in some embodiments, the wearable system can identifythe virtual object B 2214 as the target virtual object to be movedinside of the FOV based on multimodal inputs. For example, the wearablesystem can make the determination based on the user's eye gaze andpositions of the virtual objects. The wearable system can set the targetvirtual object as an object that's on the user's direction of gaze andis the closet object to the user.

Example Processes of Interacting with a Virtual Object Using MultimodalUser Inputs

FIG. 23 illustrates an example process of interacting with a virtualobject using multimodal inputs. The process 2300 can be executed by thewearable system described herein. For example, the process 2300 may beexecuted by the local processing & data module 260, remote processingmodule 270, and the central runtime server 1650, alone or incombination.

At block 2310, the wearable system can optionally detect an initiationcondition. The initiation can be a user-initiated input which canprovide an indication that the user intends to issue a command to thewearable system. The initiation condition may be predefined by thewearable system. The initiation condition may be a single input or acombination input. For example, the initiation condition may be a voiceinput, such as, e.g., by saying the phrase “Hey, Magic Leap”. Theinitiation condition can also be gesture based. For example, thewearable system can detect the presence of an initiation condition whena user's hand is detected within the FOV of the world camera (or the FOVof the user). As another example, the initiation condition may be aspecific hand motion, such as, e.g., a snap of the fingers. Theinitiation condition can also be detected when a user actuates a userinput device. For example, a user can click on a button on a user inputdevice indicating that the user will issue a command. In certainimplementations, the initiation condition may be based on multimodalinputs. For example, both a voice command and a hand gesture may berequired for the wearable system to detect the presence of theinitiation condition.

The block 2310 is optional. In some embodiments, the wearable system mayreceive and start parsing multimodal inputs without the detection of theinitiation condition. For example, when a user is watching a video, thewearable system may intake the user's multimodal inputs to adjust thevolume, fast forward, rewind, skip to the next episode, etc., withoutrequiring the user to first provide the initiation condition.Advantageously, in some embodiments, the user may not need to wake upthe video screen (e.g., so that the video screen can present the timeadjustment or volume adjustment tools) before the user can interact withthe video screen using multimodal inputs.

At block 2320, the wearable system can receive multimodal inputs for auser interaction. The multimodal inputs may be direct or indirectinputs. Example input modes may include voice, head pose, eye gaze,gesture (on a user input device or in the air), inputs on a user inputdevice (such as, e.g., a totem), user's environment, or characteristicsof objects (physical or virtual objects) in the 3D space.

At block 2330, the wearable system can parse the multimodal inputs toidentify a subject, a command, and a parameter of the user interaction.For example, the wearable system can assign confidence scores tocandidate target virtual objects, target commands, and target parametersand select the subject, command, and parameters based on the highestconfidence scores. In some embodiments, one input mode may be theprimary input mode while another input mode may be the secondary inputmode. The inputs from the secondary input mode may supplement the inputfrom the primary input mode to ascertain a target subject, command, orparameter. For example, the wearable system may set the head pose as theprimary input mode and set the voice command as the secondary inputmode. The wearable system can first interpret the inputs from primaryinput mode as much as possible and then interpret the additional inputsfrom the secondary input mode. If the additional input is interpreted tosuggest a different interaction from the inputs of the primary input,the wearable system can automatically provide a disambiguation prompt tothe user. The disambiguation prompt may request the user to select thedesired task from: the interpretation of the primary input oralternative options based on the interpretation of the secondary input.Although this example is described with reference to a primary inputmode and a second input mode, in various situations, there may be morethan two input modes. The same technique can also be applicable on athird input mode, a fourth input mode, and so forth.

At block 2340, the wearable system can execute the user interactionbased on the subject, command, and the parameter. For example, themultimodal inputs may include an eye gaze and a voice command “put thatthere”. The wearable system can determine that the subject of theinteraction is the object that the user is currently interacting with,the command is “put”, and the parameter is the center of the user'sfield of fixation (determined based on the user's eye gaze direction).Accordingly, the user can move the virtual object that the user iscurrently interacting with to the center of the user's field offixation.

Examples of Setting Direct Input Modes Associated with a UserInteraction

In some situations, such as when the user is interacting with a wearablesystem using poses, gestures, or voices, there is a risk that otherpeople near the user could “hijack” the user's interaction by issuing acommand using these direct inputs. For example, a user A could standnear a user B in a park. The user A can interact with an HMD using voicecommands. The user B can hijack the user A's experience by saying “takea picture”. This voice command issued by user B can cause the user A'sHMD to take a picture even though user A never intended to take apicture. As another example, user B could perform a gesture within theFOV of a world camera of the user A's HMD. This gesture can cause theuser A's HMD to go to a home page, for example, while the user A isplaying a video game.

In some implementations, the input can be analyzed to determine if theinput originated from the user. For example, the system can applyspeaker recognition techniques to determine whether the command “take apicture” was said by the user A or the hijacker B. The system may applycomputer vision techniques to determine whether the gesture was made byuser A's hand or by the hijacker B's hand.

Additionally or alternatively, to prevent security breaches andinterruptions of a user's interactions with the wearable system, thewearable system can automatically set available direct input modes basedon indirect inputs or require multiple modes of direct inputs before acommand is issued. FIG. 24 illustrates an example of setting directinput modes associated with a user interaction. Three direct inputs:voice 2412, head pose 2414, and hand gestures 2416 are illustrated inFIG. 24. As described further below, the slider bars 2422, 2424, and2426 represent an amount by which each input is weighted in determininga command. If the slider is all the way toward the right, the input isgiven full weight (e.g., 100%), if the slider is all the way to theleft, the input is given zero weight (e.g., 0%), and if the slider is inbetween these extreme settings, the input is given partial weight (e.g.,20% or 80% or some other value intermediate values, such as a valuebetween 0 and 1). In this example, the wearable system can be set torequire both voice commands 2422 and hand gestures 2426 (while not usinghead pose 2414) before a command is executed. Accordingly, the wearablesystem may not execute a command if the voice commands 2442 and thegesture 2426 indicate different user interactions (or virtual objects).By requiring both types of inputs, the wearable system can reduce thelikelihood that someone else hijacks the user's interaction.

As another example, one or more input modes may be disabled. Forexample, when a user is interacting with a document processingapplication, the head pose 2414 may be disabled as an input mode, asshown in FIG. 24 where the head pose slider 2424 is set to 0.

Each input may be associated with an authentication level. In FIG. 24,the voice 2412 is associated with the authentication level 2422; thehead pose 2414 is associated with the authentication level 2424; and thehand gesture 2416 is associated with the authentication level 2426. Theauthentication level may be used to determine whether an input isrequired for a command to be executed or whether an input is disabled orwhether the input is given a partial weight (between being fully enabledor fully disabled). As illustrated in FIG. 24, the authentication levelsof the voice 2412 and the hand gestures 2416 are set all the way to theright (which is associated with the maximum authentication level),suggesting that these two inputs are required for a command to issue. Asanother example, the authentication level of a head pose is set all theway to the left (which is associated with the minimum authenticationlevel). This suggests that head pose 2414 is not required for a commandto issue even though the head pose 2414 may still be used to determine atarget virtual object or a target user interface operation. In somesituations, by setting the authentication level to the minimum, thewearable system may disable head pose 2414 as an input mode.

In certain implementations, the authentication level may also be used tocalculate confidence levels associated with a virtual object. Forexample, the wearable system may assign a higher value to an input modewhich has a higher authentication level, while assigning a lower valueto an input mode which has a lower authentication level. As a result,when aggregating confidence scores from multiple input modes forcalculating an aggregated confidence score for a virtual object, theinput mode with a higher authentication level may have more weight inthe aggregated confidence score than the input mode with a lowerauthentication level.

The authentication levels can be set by a user (through inputs or via asetup panel) or can be set automatically by the wearable system, e.g.,based on indirect inputs. The wearable system may require more inputmodes when a user is in a public place while requiring fewer input modeswhen a user is in a private place. For example, the wearable system mayrequire both voice 2412 and hand gestures 2416 when the user is on asubway. However, when the user is at home, the wearable system mayrequire only the voice 2412 for issuing a command. As another example,the wearable system may disable the voice command when the user is in apublic park, thereby providing privacy to the user's interaction. Butthe voice command may still be available when the user is at home.

Although these examples are described with reference to setting directinput modes, similar techniques can also be applied to setting indirectinput modes as part of the multimodal input. For example, when a user isusing public transportation (such as, e.g., a bus), the wearable systemmay be configured to disable geolocation as an input mode because thewearable system may not know accurately where the user specifically sitsor stands on the public transportation.

Additional Example User Experiences

In addition to the examples described herein, this section describesadditional user experiences with multimodal inputs. As a first example,the multimodal inputs can include a voice input. For example, the usercan say a voice command such as “Hey Magic Leap, call her”, which isreceived by an audio sensor 232 on the HMD and parsed by the HMD system.In this command, the user can initiate the task (or provide aninitiation condition) by saying “Hey Magic Leap”. “Call” can be apreprogrammed word so the wearable system knows it should make atelephone call (rather than initiating a video call). In certainimplementations, these pre-programmed words can also be referred to as“hotwords” or “carrier phrases,” which the system recognizes asindicating the user wants to take a particular action (e.g., “Call”) andwhich may alert the system to accept further input to complete thedesired action (e.g., identify a person (“her”) or a telephone numberafter the word “Call”). The wearable system can use the additionalinputs to identify who “her” is. For example, the wearable system canuse eye tracking to see which contact on the virtual contact list or theuser's phone that the user is looking at. The wearable system can alsouse head pose or eye tracking to determine if the user is lookingdirectly at a person the user wants to call. In certain embodiments, thewearable system can utilize facial recognition techniques (e.g., usingthe object recognizers 708) to determine the identity of the person thatthe user is looking at.

As a second example, the user can have a virtual browser placed directlyon a wall (e.g., the display 220 of the wearable system can project thevirtual browser as if it is overlaid on the wall). The user can reachhis or her hand out and provide a tap gesture on a link in the browser.Since the browser appears to be on the wall, the user may tap the wallor tap in space such that the projection of the user's finger appears totap the wall to provide the indication. The wearable system can usemultimodal inputs to identify the link that the user intends to click.For example, the wearable system can use gesture detection (e.g., viadata acquired by the outward-facing imaging system 464), a head posebased cone-cast, and an eye gaze. In this example, the gesture detectionmay be less than 100% accurate. The wearable system can improve thegesture detection with data acquired from the head pose and eye gaze toincrease the gesture tracking's accuracy. For example, the wearablesystem can identify a radius where the eyes are most likely focusingbased on data acquired by the inward-facing imaging system 462. Incertain embodiments, the wearable system can identify the user's fieldof fixation based on the eye gaze. The wearable system can also useindirect input such as environment features (e.g., the location of thewall, the characteristics of the browser or the webpage, etc.) toimprove gesture tracking. In this example, the wall may be representedby a planar mesh (which may be previously stored in the map 920 of theenvironment), the wearable system can determine the user's hand positionin view of the planar mesh to determine the link that the user istargeting and selecting. Advantageously, in various embodiments, bycombining multiple modes of inputs, the accuracy required for one modeof input for a user interaction may be reduced as compared to a singlemode of input. For example, the FOV camera may not need to have veryhigh resolution for hand gesture recognition because the wearable systemcan supplement hand gestures with head pose or eye gaze to determine theintended user interaction.

Although the multimodal inputs in the examples above include an audioinput, the audio input is not required for the multimodal inputinteractions described above. For example, a user can use a 2D-touchswipe gesture (on a totem, for example) to move a browser window fromone wall to a different wall. The browser may initially be on the leftwall. The user can select the browser by actuating the totem. The usercan then look at the right wall and make a right-swipe gesture on thetouchpad of the totem. The swipe on the touchpad is loose and inaccuratebecause a 2D swipe by itself doesn't translate easily/well to a 3Dmovement. However, the wearable system can detect a wall (e.g., based onenvironmental data acquired by the outward-facing imaging system) anddetect the point where the user is specifically looking on the wall(e.g., based on eye gaze). With these three inputs (touch-swipe, gaze,environment features), the wearable system can gracefully place thebrowser at a location with high confidence that it is where the userwanted browser window to go.

Additional Examples of Head Pose as a Multimodal Input

In various embodiments, the multimodal inputs can support a totem freeexperience (or an experience where a totem is used infrequently). Forexample, multimodal inputs can include a combination of head pose andvoice control which can be used to share or search for a virtual object.The multimodal inputs can also use a combination of head pose andgestures to navigate various user interface planes and virtual objectswithin a user interface plane. A combination of head pose, voice, andgesture, can be used to move objects, conduct social networkingactivities (e.g., initiate and conduct a telepresence session, sharingposts), browse information on a webpage, or control a media player.

FIG. 25 illustrates an example of user experience with multimodal input.In the example scene 2500 a, the user 2510 can target and select theapplications 2512 and 2514 with a head pose. The wearable system candisplay a focus indicator 2524 a indicating that the user is currentlyinteracting with the virtual object with head pose. Once the userselects the application 2514, the wearable system may show a focusindicator 2524 a for the application 2514 (such as, e.g., a targetgraphic as shown in FIG. 25, a halo around the application 2514 orbringing the virtual object 2514 to appear closer to the user). Thewearable system can also change the focus indicator's appearance fromthe focus indicator 2524 a to the focus indicator 2524 b (e.g., thearrow graphic shown in the scene 2500 b) indicating that theinteractions by user input device 466 also become available after theuser selects with virtual object 2514. Voice and gesture interactionsextend this interaction pattern of head pose plus hand gestures. Forexample, when a user issues a voice command, the application targetedwith head pose may respond to or be manipulated by the voice command.Additional examples of interacting with virtual objects with acombination of, for example, head pose, hand gestures, and voicerecognition are described in U.S. application Ser. No. 15/296,869, filedOct. 18, 2016, titled “SELECTING VIRTUAL OBJECTS IN A THREE-DIMENSIONALSPACE”, published as U.S. Pat. Pub. No. 2017/0109936, the disclosure ofwhich is hereby incorporated by reference herein in its entirety.

The head pose may be integrated with voice control, gesture recognition,and environmental information (e.g., mesh information) to providehands-free browsing. For example, a voice command of “Search for FortLauderdale” will be handled by a browser if the user is using head poseto target the browser. If the user is not targeting a particularbrowser, the wearable system can also handle this voice command withoutgoing through a browser. As another example, when the user says “Sharethis with Karen”, the wearable system will execute the share action onan application that the user is targeting (e.g., using head pose, eyegaze, or gestures). As another example, the voice control can executebrowser window functions, such as, e.g., “Go to Bookmarks”, while thegestures may be used to perform basic navigation of a webpage such as,e.g., clicks and scrolls.

Multimodal inputs can also be used to launch and move a virtual objectwithout needing a user input device. The wearable system can usemultimodal inputs, such as, e.g., gesture, voice, and gaze, to naturallyplace content near a user and the environment. For example, the user canuse voice to open an unlaunched application when a user is interactingwith the HMD. The user can issue a voice command by saying “Hey MagicLeap, launch the Browser.” In this command, the initiation conditionincludes the presence of the invocation phrase “Hey Magic Leap”. Thecommand can be interpreted to include “launch” or “open” (which may beinterchangeable commands). The subject of this command is theapplication name e.g., “browser”. This command, however, does notrequire a parameter. In some embodiments, the wearable system canautomatically apply a default parameter, such as e.g., placing thebrowser in the user's environment (or the user's FOV).

The multimodal inputs can also be used to perform basic browsercontrols, such as, e.g., opening bookmarks, opening a new tab,navigating to history, etc. The ability to reference web content inhands-free or hands-full multi-tasking scenarios can empower users to bemore informed and productive. For example, a user, Ada, is a radiologistreading films in her office. Ada can navigate the web with voice andgesture to bring up reference material while reading the films, whichreduces her need to move a mouse back and forth to switch between thefilms and the reference materials on the screen. As another example, auser, Chris, is cooking a new recipe from a virtual browser window. Thevirtual browser window can be placed on his cabinet. Chris can use avoice command to pull up a bookmarked recipe while he starts choppingfood.

FIG. 26 illustrates an example user interface with a variety ofbookmarked applications. A user can select an application on the userinterface 2600 by saying the name of the application. For example, theuser can say “open food” to launch the food application. As anotherexample, the user can say “open this”. The wearable system can determinethe user's direction of gaze and identify an application on the userinterface 2600 that intersects with the user's direction of gaze. Thewearable system can accordingly open the identified application.

A user can also use a voice to issue a search command. The searchcommand can be performed by an application that the user is currentlytargeting. If the object does not currently support a search command,the wearable system may perform a search within a data store of thewearable system or search for the information via a default application(such as, e.g., via a browser). FIG. 27 illustrates an example userinterface 2700 when a search command is issued. The user interface 2700shows both an email application and a media watching application. Thewearable system may determine (based on the user's head pose) that theuser is currently interacting with the email application. As a result,the wearable system may automatically translate the user's voice commandinto a search command in the email application.

Multimodal inputs can also be used for media controls. For example, thewearable system can use voice and gesture controls to issue commandssuch as, e.g., play, pause, mute, fast forward, and rewind, forcontrolling a media player in an application (such as screens). Theusers can use the voice and gesture controls with a media applicationand set the totem aside.

Multimodal inputs can further be used in a social networking context.For example, a user can start conversations and share experiences (e.g.,virtual images, documents, etc.) without a user input device. As anotherexample, users can participate in a telepresence session and set aprivate context such that the users can feel comfortable for usingvoices to navigate the user interface.

Accordingly, in various implementations, the system may utilizemultimodal inputs such as: head pose plus voice (e.g., for informationsharing and general application searching), head pose plus gesture(e.g., for navigation in applications), or head pose plus voice plusgesture (e.g., for “put that there” functionality, media playercontrols, social interactions, or browser applications).

Additional Examples of Gesture Control as Part of Multimodal Inputs

There may be two, non-limiting and non-exclusive, classes of gestureinteractions: event gestures and dynamic hand tracking. Event gesturescan be in response to an event while a user is interacting with an HMD,such as, e.g., a catcher throwing a sign to a pitcher at a baseball gameor a thumbs-up sign at a browser window to cause the wearable system toopen a share dialogue. The wearable system can follow one or moregesture patterns that the user performs and respond to the eventaccordingly. Dynamic hand tracking can involve tracking the user's handwith low latency. For example, the user can move a hand over the user'sFOV and a virtual character may follow the movement of the user'sfinger.

The quality of gesture tracking may depend on the type of userinteraction. The quality may involve multiple factors, e.g., robustness,responsiveness, and ergonomics. In some embodiments, the event gesturesmay be near-perfect robustness. The threshold for minimum acceptablegesture performance may be a bit lower in social experiences,bleeding-edge interactions, and third party applications, since theaesthetics of these experiences can tolerate faults, interruptions, lowlatency, etc., but gesture recognition can still be highly performant inthese experiences to maintain the responsiveness.

To increase the likelihood that the wearable system is responsive to auser's gesture, the system can reduce or minimize latency for gesturedetection (for both event gestures and dynamic hand tracking). Forexample, the wearable system can reduce or minimize latency by detectingwhen the user's hand is within view of the depth sensor, automaticallyswitching the depth sensor to the appropriate gesture mode, and thengiving feedback to the user when he or she can perform the gesture.

As described herein, gestures can be used in combination with otherinput modes to launch, select, and move an application. Gesture can alsobe used to interact with virtual objects within an application, such asby tapping, scrolling in the air or on a surface (e.g., on a table or awall).

In certain embodiments, the wearable system can implement a socialnetworking tool which can support gesture interactions. A user canperform semantic event gestures to enrich communication. For example,the user can wave a hand in front of the FOV camera and a wave animationcan accordingly be sent to the person the user is chatting with. Thewearable system can also provide virtualization of a user's hands withdynamic hand tracking. For example, a user can hold up his or her handsin front of his or her FOV and get visual feedback that his or her handsare being tracked to animate his or her avatar's hands.

The hand gestures can also be used as part of the multimodal inputs formedia player controls. For example, the user can use a hand gesture toplay or to pause a video stream. The user can perform the gesturemanipulation away from the device (e.g., a television) playing thevideo. Upon detecting the user's gesture, the wearable system canremotely control the device based on the user's gesture. The user canalso look at the media panel and the wearable system can use the user'shand gesture in combination with the user's direction of gaze to updatethe parameters of the medial panel. For example, a pinch (ok) gesturemay suggest a “play” command a first gesture may suggest a “pause”command. The user can also close up the menu by waving one of the armsin front of the FOV camera. Examples of hand gestures 2080 are shown inFIG. 20.

Additional Examples of Interacting with Virtual Objects

As described herein, the wearable system can support various multimodalinteractions with objects (physical or virtual) in the user'senvironment. For example, the wearable system can support direct inputsfor interactions with found objects, such as targeting, selecting,controlling (e.g., the movement or properties) the found objects. Theinteractions with the found objects can also include interactions withfound object geometries or interactions with found object connectedsurfaces.

Direct inputs are also supported for interactions with flat surfaces,such as targeting and selecting wall or table top. The user can alsoinitiate various user interface events, such as, e.g., touch events, tapevents, swipe events, or scroll events. The user can manipulate 2D userinterface elements (e.g., panels) using direct interactions, such as,e.g., panel scrolling, swiping, and selecting elements (e.g., virtualobjects or user interface elements such as buttons) within a panel. Theuser can also move or resize the panel using one or more direct inputs.

Direct inputs can further be used to manipulate objects that are atdifferent depths. The wearable system can set various thresholddistances (from the user) to determine the region of the virtualobjects. With reference to FIG. 21, the objects that are within thedashed circle 2122 may be considered as objects in the near-field, theobjects that are within the dashed circle 2124 (but are outside of thedashed circle 2122) may be considered as objects in the mid field, andthe objects that are outside of the dashed circle 2124 may be consideredas objects in the far field. The threshold distance between the nearfield and the far field may be, e.g., 1 m, 2 m, 3 m, 4 m, 5 m, or more,and may depend on environment (e.g., larger in an outdoor park than anindoor office cubicle).

The wearable system can support various 2D or 3D manipulations ofvirtual objects in the near field. Example 2D manipulations may includemoving or resizing. Example 3D manipulations may include placing thevirtual objects in the 3D space such as by pinching, drawing, moving, orrotating the virtual objects. The wearable system can also supportinteractions with virtual objects in the mid field such as, e.g.,panning and repositioning the object in the user's environment,performing a radial motion of the object, or moving the object into thenear field or the far field.

The wearable system can also support continuous fingertip interactions.For example, the wearable system can allow the user's finger to pointlike an attractor, or pinpoint an object and perform a push interactionon the object. The wearable system can further support fast poseinteractions, such as, e.g., hand surface interactions or hand contourinteractions.

Additional Examples of Voice Command in the Context of Social Networkand Sharing

The wearable system can support voice commands as an input for a socialnetworking (or messaging) application. For example, the wearable systemcan support voice commands for sharing information with contacts ormaking calls with contacts.

As an example of starting a call with a contact, the user can use avoice command such as “Hey Magic Leap, call Karen.” In this command,“Hey Magic Leap” is the invocation phrase, the command is “call”, andthe parameter of the command is the name of the contact. The wearablesystem can automatically use a messenger application (as the subject) toinitiate the call. The command “call” may be associated with tasks, suchas, e.g., “start a call with”, start a chat with”, etc.

If the user says “Start a call” and then says a name, the wearablesystem can attempt to recognize the name. If the wearable system doesnot recognize the name, the wearable system can communicate a message tothe user for the user to confirm the name or contact information. If thewearable system recognizes the name, the wearable system may present adialog prompt which the user can confirm/deny (or cancel) the call, orprovide an alternative contact.

The user can also start a call with several contacts with a list offriends. For example, the user can say “Hey Magic Leap, start a groupchat with Karen, Cole, and Kojo.” The group chat command may beextracted from the phrase “start a group chat” or may be from a list offriends provided by the user. While a user is in a call, the user canadd another user to the conversation. For example, the user can say “HeyMagic Leap, invite Karen” where the phrase “invite” can be associatedwith an invite command.

The wearable system can share virtual objects with a contact using voicecommands. For example, the user can say “Hey Magic Leap, share Screenswith Karen” or “Hey Magic Leap, share that with David and Tony.” Inthese examples, the word “share” is a share command. The word “screens”or “that” may be a reference to a subject which the wearable system candetermine based on multimodal inputs. The names such as “Karen”, “Davidand Tony” are the parameters of the command. In some embodiments, whenthe voice command provided by the user includes the word “share” with anapplication reference and a contact, the wearable system may provide aconfirmation dialog to ask the user to confirm whether the user wants toshare the application itself or share a subject via the referencedapplication. When the user issues the voice command including the word“share”, an application reference, and a contact, the wearable systemcan determine whether the application name is recognized by the wearablesystem or whether the application exists on the user's system. If thesystem does not recognize the name or the application does not exist inthe user's system, the wearable system may provide a message to theuser. The message may suggest the user to try the voice command again.

If the user provides deictic or anaphoric references (e.g., “this” or“that”) in the voice command, the wearable system can use multimodalinputs (e.g., the user's head pose) to determine whether the user isinteracting with an object that can be shared. If the object cannot beshared, the wearable system may prompt an error message to the user ormove to a second mode of input, such as gestures, to determine whichobject should be shared.

The wearable system can also determine whether the contact with whom theobject is shared can be recognized (e.g., as part of the user's contactlist). If the wearable system recognizes the name of the contact, thewearable system can provide a confirmation dialogue to confirm that theuser wants to proceed with sharing. If the user confirms, the virtualobject can be shared. In some embodiments, the wearable system can sharemultiple virtual objects associated with an application. For example,the wearable system can share a whole album of pictures or share themost recently viewed picture in response to the user's voice command. Ifthe user denies sharing, the share command is canceled. If the userindicates that the contact is wrong, the wearable system may prompt theuser to speak the contact's name again or select a contact from a listof available contacts.

In certain implementations, if the user says “Share” and says anapplication reference but doesn't specify a contact, the wearable systemmay share the application locally with people in the user's environmentwho have access to the user's file. The wearable system may also replyand request the user to input a name using one or more of the inputmodes described herein. Similar to the social networking example, theuser can issue a voice command to share a virtual object with onecontact or a group of contacts.

A challenge in making calls via voice is when the Voice user interfaceincorrectly recognizes or fails to recognize a contact's name. This canbe especially problematic with less common or non-English names, e.g.,like Isi, Ileana, etc. For example, when a user says a voice commandincludes the name of a contact (such as “Share Screens with Ily”), thewearable system may not be able to identify the name “Ily” or itspronunciation. The wearable system can open a contacts dialogue with aprompt such as, e.g., “Who?” The user can try again with voice tospecify “Ily”, spell the name out “I-L-Y” using voice or a user inputdevice, or use a user input device to quickly select names from a panelof available names. The name “Ily” may be a nickname for Ileana, who hasan entry in the user's contacts. Once the user instructs the system that“Ily” is the nickname, the system may be configured to “remember” thenickname by automatically associating the nickname (or the pronunciationor audio pattern associated with the nickname) with the friend's name.

Additional Examples of Selecting and Moving a Virtual Object Using aVoice Command

A user can naturally and quickly manage the placement of a virtualobject in the user's environment using multimodal inputs, such as, e.g.,a combination of eye gaze, gestures, and voice. For example, a usernamed Lindsay sits down at the table and gets ready to do some work. Sheopens her laptop and starts up the desktop-Monitors app on her computer.As the computer is loading, she reaches her hand out above the laptopscreen and says “Hey Magic Leap, put Monitors here.” In response to thisvoice command, the wearable system can automatically launch the monitorscreens and place them above her laptop. However, when Lindsay says “Putscreens there” while looking over at the wall on the other side of theroom, the wearable system can automatically place the screens on thewall across from her. Lindsay could also say “Put halcyon here,” whilelooking at her desk. The halcyon was initially on her kitchen table, butin response to the voice command, the wearable system can automaticallymove it to her table surface. As she works, she can use a totem tointeract with these objects and adjust their scales to her preference.

The user can use voice to open an unlaunched application at any point inthe user's environment. For example, the user can say “Hey Magic Leap,launch the Browser.” In this command “Hey Magic Leap” is the invocationword, the word “launch” is a launch command, and the word “Browser” isan application of the subject. The “launch” commands may be associatedwith the words “launch”, “open”, “play”. For example, the wearablesystem can still identify the launch command when the user says “openthe browser”. In certain embodiments, an application may be an immersiveapplication which can provide a 3D virtual environment to a user as ifthe user is part of the 3D virtual environment. As a result, when theimmersive application is launched, the user may be positioned as if heis in the 3D virtual environment. In certain implementations, animmersive application also includes a store application. When the storeapplication is launched, the wearable system can provide a 3D shoppingexperience for the user so that the user can feel as if he is shoppingin a real store. In contrast to the immersive application, anapplication may be a landscape application. When the landscapeapplication is launched, it may be placed to where it would be placed iflaunched via totem in a launcher. As a result, the user can interactwith the landscape application, but the user may not feel that he ispart of the landscape application.

The user can also use a voice command to launch a virtual application ina specified location in the user's FOV or the user can move analready-placed virtual application (e.g., a landscape application) to aspecific location in the user's FOV. For example, the user can say “HeyMagic Leap, Put the browser here,” “Hey Magic Leap, Put the browserthere,” “Hey Magic Leap, Put this here,” or “Hey Magic Leap, Put thatthere.” These voice commands include the invocation word, the putcommand, the application name (which is a subject), and a location cue(which is a parameter). The subject may be referenced based on the audiodata, for example, based on the name of application spoken by the user.The subject may also be identified based on head pose or eye gaze whenthe user says the word “this” or “that” instead. To facilitate thisvoice interaction, the wearable system can make, for example, twoinferences: (1) which application to launch and (2) where to place theapplication.

The wearable system can use the put command and the application name toinfer which application to launch. For example, if the user says anapplication name that the wearable system doesn't recognize, thewearable system may provide an error message. If the user says anapplication name that the wearable system recognizes, the wearablesystem can determine whether the application has already been placedinto the user's environment. If the application is already shown in theuser's environment (such as, e.g., in the user's FOV), the wearablesystem can determine how many instances of the applications there are inthe user's environment (e.g., how many browser windows are open). Ifthere is just one instance of the target application, the wearablesystem can move the application to the location specified by the user.If there is more than one instance of the spoken application in theenvironment, the wearable system can move all instances of theapplication to the specified location or the most recently used instanceto the specified location. If the virtual application has not alreadybeen placed in the user's environment, the system can determine whetherthe application is a landscape application, an immersive application, ora store application (in which the user can download or purchase otherapplications). If the application is a landscape application, thewearable system can launch the virtual application at a specifiedlocation. If the application is an immersive application, the wearablesystem can place a shortcut of the application at the specified positionbecause the immersive application does not support the functions oflaunching at a specified location in the user's FOV. If the applicationis the store application, the system may place a mini store at thespecified position since the store application may require full 3Dimmersion of the user into the virtual world and therefore do notsupport launching at a specific location in the user's environment. Themini store may include brief summaries or icons of virtual objects inthe store.

The wearable system can use a variety of inputs to determine where toplace the application. The wearable system can parse the syntax in theuser's command (e.g., “here” or “there”), determine intersections ofvirtual objects in the user's environment with a head pose based raycast(or cone cast), determine the user's hand position, determine planarsurface mesh or environment planar mesh (e.g., a mesh associated with awall or a table), etc. As an example, if the user says “here”, thewearable system can determine the user's hand gesture, such as whetherthere is a flat open hand in the user's FOV. The wearable system canplace the object at the position of the user's flat hand and at arendering plane that is near the user's hand reach. If there are no flatopen hands in the FOV, the wearable system can determine whether a headpose (e.g., the direction of a head pose based cone cast) isintersecting with surface-planar mesh that is within the user'sarms-reach. If the surface-planar mesh exists, the wearable system canplace the virtual object at the intersection of the direction of thehead pose and the surface-planar mesh at a rendering plane that iswithin the user's arms-reach. The user can place the object flat on thesurface. If there is no surface planar mesh, the wearable system mayplace the virtual object at a rendering plane having distance somewherebetween within-arms-reach and optimal reading distance. If the user says“there”, the wearable system can perform similar operations as when theuser says “here”, except that if there is no surface-planar mesh that iswithin the user's arms-reach, the wearable system may place a virtualobject at a rendering plane in the mid field.

Once the user says “Put the Application . . . .”, the wearable systemcan immediately provide predictive feedback to a user to show where thevirtual object would be placed based on available inputs if the usersays either “here” or “there”. This feedback could be in the form of afocus indicator. For example, the feedback may include a small floatingtext bubble saying “here” at the hand, mesh, or a planar surface whichintersects with the user's head pose direction at a rendering plane within the user's arms reach. The planar surface may be located in the nearfield if the user's command is “here” while in the mid or far field ifthe user's command is “there”. This feedback could be visualized like ashadow or the outline of the visual object.

The user can also cancel the interaction. An interaction may be canceledin two ways in various cases: (1) a command failed to be completed by ann second timeout or (2) input a canceling command, such as, e.g., saying“no”, “never mind’, or “cancel”.

Examples of Interacting with Text Using a Combination of User Inputs

Free form text input in a mixed reality environment, particularly inputof long string sequences, using traditional interaction modalities canbe problematic. As an example, systems that rely entirely upon automatedspeech recognition (ASR), especially in a “hands-free” environmentlacking input or interface devices such as keyboard, handheld controller(e.g., totem) or mouse, can be difficult to use for text editing (e.g.,to correct ASR errors endemic to speech recognition technology itselfsuch as an incorrect transcription of the user's speech). As anotherexample, a virtual keyboard in a “hands-free” environment may requirerefined user control and can cause fatigue if used as the primary formof user input.

The wearable system 200 described herein can be programmed to allow auser to naturally and quickly interact with virtual text usingmultimodal inputs, such as, e.g., a combination of two or more of:voice, eye gaze, gestures, head poses, totem inputs, etc. The phrase“text” as used herein can include a letter, a character, a word, aphrase, a sentence, a paragraph, or other types of free-form text. Textcan also include graphics or animations, e.g., emoji, ideograms,emoticons, smileys, symbols, etc. Interactions with the virtual text caninclude composing, selecting (e.g., selecting a portion of or all text),or editing text (e.g., change, copy, cut, paste, delete, clear, undo,redo, insert, replace, etc.), alone or in combination. By utilizing acombination of user inputs, the systems described herein providesignificant improvements in speed and convenience over single-inputsystems.

The multimodal text interaction techniques described herein can beapplied in any dictation scenario or application (e.g., in which thesystem simply transcribes user speech rather than applying any semanticevaluation, even if that transcription is part of another task that doesrely on semantic evaluation), Some example applications can include amessaging application, a word processing application, a gamingapplication, a system configuration application, etc. Examples of usecases can include a user writing a text message to be sent to a contactthat may or may not be in the user's contact list; a user writing aletter, an article, or other textual content; a user posting and sharingcontent on a social media platform; and a user completing or otherwisefilling out a form using the wearable system 200.

A system utilizing a combination of user inputs need not be a wearablesystem. If desired, such a system may be any suitable computing systemsuch as a desktop computer, a laptop, a tablet, a smart phone, oranother computing device having multiple user input channels such askeyboards, trackpads, microphones, eye or gaze tracking systems, gesturerecognition systems, etc.

Examples of Composing a Text with Multimodal User Inputs

FIGS. 28A-28F illustrate an example user experience of composing andediting a text based on a combination of inputs such as, e.g. voicecommands or eye gaze. As described herein, the wearable system candetermine the user's gaze direction based on images acquired by theinward-facing imaging system 462 shown in FIG. 4. The inward-facingimaging system 462 may determine the orientation of one or both of theuser's pupils and may extrapolate the line or lines of sight of theuser's eye or eyes. By determining the lines of sight of both eyes ofthe user, the wearable system 200 can determine the three-dimensionalposition in space in which the user is looking.

The wearable system can also determine a voice command based on dataacquired from the audio sensor 232 (e.g., a microphone) shown in FIG.2A. The system may have an automated speech recognition (ASR) enginethat converts the spoken input 2800 into text. The speech recognitionengine may use natural language understanding in converting the spokeninput 2800 into text, including isolating and extracting message textfrom a longer utterance.

As shown in FIG. 28A, the audio sensor 232 can receive a phrase 2800spoken by a user. As illustrated in FIG. 28A, the phrase 2800 mayinclude a command, such as “Send a message to John Smith saying that,”as well as the parameters of the command such as, e.g., composing andsending a message, and the destination of the message as John Smith. Thephrase 2800 can also include the content of the message that is to becomposed. In this example, the content of the message can include “I'mflying in from Boston and will be there around seven o'clock; Period;Let's meet at the corner near the office.” Such content can be obtainedby parsing the audio data using an ASR engine (which can implementnatural language understanding to isolate and extract message contentand punctuation (e.g., “Period”) from the user's utterance). In someexamples, punctuation may be processed for presentation within thecontext of a transcribed string (e.g., “two o'clock” may be presented as“2:00” or “question mark” may be presented as “?”) The wearable systemcan also tokenize the text string, such as by isolating discrete wordsin the text string, and display the result, such as by displaying thediscrete words, in the mixed reality environment.

However, automatic speech recognition may be susceptible to errors insome situations. As illustrated in FIG. 28B, a system using an ASRengine may produce results that do not precisely match the user's spokeninput, for various reasons including poor or idiosyncraticpronunciation, environmental noise, homonyms and other similar soundingwords, hesitations or disfluency, and vocabulary that is not in theASR's dictionary (e.g., foreign phrases, technical terms, jargon, slang,etc.). In the example of FIG. 28B, the system properly interpreted thecommand aspect of the phrase 2800 and generated a message with a header2802 and a body 2804. However, in the body 2804 of the message, thesystem incorrectly interpreted the user's utterance of “corner” as“quarter,” which are somewhat similar sounding. In systems that relyentirely upon voice inputs, it would be difficult for a user to quicklyreplace the misrecognized word (or phrase) with the intended word (orphrase). However, the wearable system 200 described herein canadvantageously allow the user quickly correct the error as illustratedin FIGS. 28C-28F.

The ASR engine in the wearable system may produce text results,including at least one word, associated with a user's utterance and mayalso produce an ASR score associated with each word (or phrase) in thetext results. A high ASR score may indicate a high confidence or highlikelihood that the ASR engine correctly transcribed the user'sutterance into text, whereas a low ASR score may indicate a lowconfidence or low likelihood that the ASR engine correctly transcribedthe user's utterance into text. In some embodiments, the system maydisplay words with low ASR scores (e.g., ASR scores below an ASRthreshold) in an emphasized manner (e.g., with background highlighting,italics or bold font, different color font, etc.), which may make iteasier for the user to identify or select incorrectly recognized words.A low ASR score for a word can indicate that the user is more likely toselect that word for editing or replacement, because there is areasonable likelihood that the ASR engine mis-recognized the word.

As shown in FIGS. 28C and 28D, the wearable system may enable the userto select the misrecognized word (or phrase) using an eye trackingsystem, such as inward-facing imaging system 462 of FIG. 4. In thisexample, the selected word may be an example of the target virtualobject described above with earlier figures.

The wearable system 200 can determine the gaze direction based on theinward-facing imaging system 462 and can cast a cone 2806 or ray in thegaze direction. The wearable system can select one or more words thatintercept with the user's direction of gaze. In certain implementations,a word may be selected when the user's gaze lingers on the erroneousword for at least a threshold time. As described above, the erroneousword may be determined at least in part by being associated with a lowASR score. The threshold time may be any amount of time sufficient toindicate that the user wants to select a particular word, but not solong as to unnecessarily delay selection. The threshold time may also beused to determine a confidence score indicating that the user desires toselect a particular virtual word. For example, the wearable system cancalculate the confidence score based on how long a user has stared at adirection/object, where the confidence score may increase as the timeduration for looking at a certain direction/object increases. Theconfidence score may also be calculated based on multimodal inputs asdescribed herein. For example, the wearable system may determine, with ahigher confidence score (than the confidence score derived from eye gazealone), if both the user's hand gesture and the eye gaze indicate a wordshould be selected.

As another example, the wearable system may calculate the confidencescore based in part on the ASR score, which may be indicative of therelative confidence of the ASR engine of a translation of a particularword, as discussed in more detail herein. For example, a low ASR enginescore may be indicative that the ASR engine has relatively lowconfidence that it correctly transcribed a spoken word. Therefore, thereis a higher probability that the user will be likely to select that wordfor editing or replacement. If the user's gaze lingers longer than athreshold time on a word that has a low ASR score, the system can assigna higher confidence score to reflect that the user has selected thatword for at least two reasons: first, the length of the eye gaze on theword and second, the fact that the word was likely mis-transcribed bythe ASR engine, both of which tend to indicate that the user is going towant to edit or replace that word.

A word may be selected if the confidence score passes a thresholdcriterion. As examples, the threshold time may be one-half a second, onesecond, one and a half seconds, two seconds, two and a half seconds,between one and two seconds, between one and three seconds, etc. Thus,the user can easily and quickly select the erroneous word, “quarter,”merely by looking at it for a sufficient time. The word may be selectedbased on a combination of eye gaze (or gesture) time together with anASR score above an ASR threshold, both of which criteria provideindications that the user is going to select that particular word.

As an example, if the results of the ASR engine include a first wordhaving a high ASR score (e.g., a word the ASR engine is relativelyconfident was correctly recognized) and a second word having a low ASRscore (e.g., a word the ASR engine is relatively confident was notcorrectly recognized) and these two words are displayed adjacent to eachother by the wearable system, the wearable system may assume that auser's gaze input that encompasses both the first and second words isactually an attempt by the user to select the second word, based on itsrelatively low ASR score, because the user is more likely to want toedit the incorrectly recognized second word than the correctlyrecognized first word. In this manner, words produced by an ASR enginewith a low ASR score, which are more likely to be inaccurate and requireediting, may be significantly easier for a user to select for editing,thus facilitating editing by the user.

Although this example describes selecting the misrecognized word usingeye gaze, another multimodal input can also be used to select a word.For example, cone casting can identify multiple words, such as “around”,“7:00”, “the”, and “quarter”, since they also intersect with a portionof the virtual cone 2806. As will further be described with reference toFIGS. 29-31, the wearable system can combine the eye gaze input withanother input (such as e.g., a gesture, a voice command, or an inputfrom a user input device 466) to select the word “quarter” as the wordfor further editing.

Upon selecting the word 2808, the system can enable editing of theselected word. The wearable system can allow a user to edit the wordusing a variety of techniques, such as, e.g., change, cut, copy, paste,delete, clear, undo, redo, insert, replace, etc. As shown in FIG. 28D,the wearable system can allow a user to change the word 2808 to anotherword. The wearable system can support a variety of user inputs forediting the word 2808, such as, e.g., by receiving additional spokeninput through a microphone to replace or delete the selected word,displaying a virtual keyboard to enable the user to type out areplacement, or receiving user input via a user input device, etc. Incertain implementations, an input may be associated with a specific typeof text editing. For example, a waving gesture may be associated withdeleting the selected text while a gesture with a finger pointing at aposition in the text may cause the wearable system to insert additionaltext at the position. The wearable system can also support a combinationof user inputs to edit the words. As will further be described withreference to FIGS. 32-35, the system can support eye gaze in combinationwith another input mode to edit the word.

In the examples of FIGS. 28D and 28E, the system may automaticallypresent the user with an array of suggested alternatives such asalternatives 2810 a and 2810 b upon a selection of the word 2808. Thesuggested alternatives may be generated by the ASR engine or otherlanguage processing engines in the system and may be based on theoriginal speech input (which may also be referred to as voice input insome embodiments), natural language understandings, context, learnedfrom user behavior, or other suitable sources. In at least someembodiments, suggested alternatives may be alternate hypothesesgenerated by the ASR engine, may be hypotheses generated by a predictivetext engine (which may try to “fill in the blanks” using the context ofadjacent words and a user's historical patterns of text), may behomophones of the original translation, may be generated using athesaurus, or may be generated using other suitable techniques. In theillustrated examples, the suggested alternatives to “quarter” include“corner” and “courter”, which may be provided by a language engine asbeing words that sound similar to “quarter.”

FIG. 28E illustrates how the system may enable the user to select adesired alternative word, such as “corner,” with eye gaze. The wearablesystem may use similar techniques as those described with reference toFIG. 28C to select the alternative word. For example, the system maytrack the user's eyes using inward-facing imaging system 462 todetermine that the user's gaze 2812 has been focused upon a particularalternative, such as alternative 2810A or “corner”, for at least athreshold time. After determining that the user's gaze 2812 was focusedon an alternative for the threshold time, the system may revise the text(the message) by replacing the originally selected word with theselected alternative word 2814, as shown in FIG. 28F. In certainimplementations, where the wearable system uses cone casting to select aword, the wearable system can dynamically adjust the size of the conebased on the density of the text. For example, the wearable system maypresent a cone with a bigger aperture (and thus with a bigger surfacearea at the away from the user) to select an alternative word forediting as shown in FIG. 28E because there are few available options.But the wearable system may present the cone with a smaller aperture toselect the word 2808 in FIG. 28C because the word 2808 is surroundedwith other words and a smaller cone can reduce the error rate ofaccidentally selecting another word.

The wearable system can provide feedback (e.g., visual, audio, haptic,etc.) to the user throughout the course of operation. For example, thewearable system can present a focus indicator to facilitate the user'srecognition of the target virtual object. For example, as shown in FIG.28E, the wearable system can provide a contrasting background 2830around the word “quarter” to show that the word “quarter” is selectedand the user is currently editing the word “quarter”. As anotherexample, as shown in FIG. 28F, the wearable system can change the fontof the word “corner” 2814 (e.g., to a bold font) to show that thewearable system has confirmed the replacement of the word “quarter” withthis alternative word “corner”. In other implementations, the focusindicator can include a cross-hair, a circle or oval surrounding theselected text, or other graphical techniques to highlight or emphasizethe selected text.

Examples of Selecting a Word with Multimodal User Inputs

The wearable system can be configured to support and utilize multiplemodes of user inputs to select a word. FIGS. 29-31 illustrate examplesof selecting a word based on a combination of eye gaze and another inputmode. Although in other examples, inputs other than eye gaze can also beused in combination with another mode of inputs for interactions withtexts.

FIG. 29 illustrates an example of selecting a word based on an inputfrom a user input device and gaze. As shown in FIG. 29, the system maycombine a user's gaze 2900 (which may be determined based on data fromthe inward-facing imaging system 462) together with a user inputreceived via a user input device 466. In this example, the wearablesystem can perform a cone cast based on the user's direction of gaze.The wearable system can confirm the selection of the word “quarter”based on the input from the user input device. For example, the wearablesystem can identify that the word “quarter” is the word that is closestto the user's gaze direction and the wearable system can confirm thatthe word quarter is selected based on the user's actuation of the userinput device 466. As another example, the cone cast can capture aplurality of words, such as, e.g., “around”, “7:00”, “the”, and“quarter”. The user can select the word, via the user input device 466,among the plurality of words for further editing. By receiving inputindependent of the user's gaze, the system may not need to wait as longbefore confidently identifying a particular word as one the user wantsto edit. After selecting a word to edit in this manner, the system maypresent alternatives (as discussed in connection with FIG. 28E) orotherwise allow the user to edit the selected word. The same process ofcombining the user's gaze with a user input received via a totem may beapplied to selecting a desired replacement word (e.g., selecting theword “corner” among the alternatives to replace the word “quarter”).Some implementations may utilize a confidence score to determine whichtext is being selected by the user. The confidence score may aggregatemultiple input modalities to provide a better determination of theselected text. For example, the confidence score may be based on thetime that the user gazes at the text, whether the user actuates the userinput device 466 when gazing at the text, whether the user points towardthe selected text, and so forth. If the confidence score passes athreshold, the wearable system can determine, with increased confidence,that the system has correctly selected the text the user wants. Forexample, to select text just with eye gaze, the system may be configuredto select the text if the gaze time exceeds 1.5 seconds. However, if theuser gazes at the text for only 0.5 seconds but simultaneously actuatesthe user input device, the system can more quickly and confidentlydetermine the selected text, which may improve the user experience.

FIG. 30 illustrates an example of selecting a word for editing based ona combination of voice and gaze inputs. The wearable system candetermine a target virtual object based on the user's gaze. As shown inFIG. 30, the system may determine that a user's gaze 3000 is directed toa particular word (in this case “quarter”). The wearable system can alsodetermine the operation to be performed on the target virtual objectbased on the user's voice command. For example, the wearable system mayreceive a user's spoken input 3010 via the audio sensor 232, mayrecognize the spoken input 3010 as a command, and may combine the twouser inputs into a command to apply the command operation (“edit”) tothe target virtual object (e.g., the word the user is focusing theirgaze upon (“quarter”)). As discussed previously, the system may presentalternative words after a user selects a word for editing. The sameprocess of combining the user's gaze with a spoken input may be appliedto selecting a desired replacement word among the alternative words toreplace the word “quarter”. As described herein, a term like “edit”represents a context-specific wakeup word that serves to invoke aconstrained system command library associated with editing for each ofone or more different user input modalities. That is, such a term, whenreceived by the system as spoken input may cause the system to evaluatesubsequently-received user input against a limited set of criteria so asto recognize editing-related commands provided by the user with enhancedaccuracy. For example, within the context of speech input, the systemmight consult a limited command-specific vocabulary of terms to performspeech recognition on subsequently-received speech input. In anotherexample, within the context of gaze or gesture input, the system mightconsult a limited command-specific library of template images to performimage recognition on subsequently-received gaze or gesture input. A termlike “edit” is sometimes referred to as a “hotword” or “carrier phrase,”and the system may include a number of pre-programmed (and optionally,user-settable) hotwords such as (in the editing context): edit, cut,copy, paste, bold, italic, delete, move, etc.

FIG. 31 illustrates an example of selecting a word for editing based ona combination of gaze and gesture inputs. As illustrated in the exampleof FIG. 31, the system may use eye gaze input 3100 together with gestureinput 3110 to select a word for editing. In particular, the system maydetermine an eye gaze input 3100 (e.g., based on data acquired by theinward-facing imaging system 462) and may identify a gesture input 3110(e.g., based on images acquired by the outward-facing imaging system464). Object recognizers such as the recognizers 708 may be used indetecting part of a user's body, such as their hand, making a gestureassociated with identification of a word for editing.

The gesture may be used alone or in combination with the eye gaze toselect a word. For example, although the cone cast can capture multiplewords, the wearable system may nevertheless identify the word “quarter”as the target virtual object because it is identified both from conecast and the user's hand gesture (e.g., a confidence score based on theeye gaze cone cast in addition to the hand gesture passes a confidencethreshold indicating the user selected the word “quarter”). As anotherexample, although the cone cast can capture multiple words, the wearablesystem may nevertheless identify the word “quarter” as the targetvirtual object because it is identified both from cone cast and is theword with the lowest ASR score from the ASR engine that lies within (ornear) the cone cast. In certain implementations, a gesture may beassociated with a command operation, as it can be associated with acommand such as “edit” or the other hotwords described herein. As anexample, the system may recognize when a user points to the same wordthey are gazing at, and interpret these user inputs as a request to editthe same word. If desired, the system may also utilize additional userinput, such as a voice command to “edit” at the same time, indetermining that the user wants to edit a particular word.

Examples of Editing a Word with Multimodal User Inputs

Once the user has selected a word for editing, the system can utilizeany desirable mode of user input to edit the selected word. The wearablesystem can allow a user to change or replace the selected word bydisplaying a list of potential alternatives and receiving user gazeinput 2812 to select an alternative word to replace the original word(see example illustrated in FIG. 28E). FIGS. 32-34 illustrate additionalexamples of editing a selected word where the selected word can beedited using multimodal inputs.

FIG. 32 illustrates an example of replacing a word based on acombination of eye gaze and speech inputs. In FIG. 32, the systemreceives a speech input 3210 from the user (through audio sensor 232 orother suitable sensor). The speech input 3210 can contain the desiredreplacement word (which may or may not be a replacement word from thelist of suggested alternatives 3200). Upon receiving the speech input3210, the wearable system can parse the input (e.g. to strip out carrierphrases like “change this to . . . ”) to identify the word spoken by theuser and replace the selected word “quarter” with the word “corner” asuttered by the user. Although in this example, the replacement is aword, in certain implementations, the wearable system can be configuredto replace the word “quarter” with a phrase or a sentence or some otherelement (e.g., an emoji). In examples where multiple words are containedwithin the eye gaze cone cast, the wearable system may automaticallyselect the word within the eye gaze cone that is closest to thereplacement word (e.g. “quarter” is closer to “corner” than “the” or“7:00”).

FIG. 33 illustrates an example of changing a word based on a combinationof voice and gaze inputs. In this example, the wearable system canreceive a speech input 3310 and determine the user's gaze direction3300. As shown in FIG. 33, the speech input 3310 includes the phrase“change it to ‘corner’”. The wearable system can parse the speech input3310 and determine that the speech input 3310 includes a commandoperation “change” (which is an example of a carrier phrase), a subject“it”, and a parameter of the command (e.g., a resulting word “corner”).This speech input 3310 can be combined with the eye gaze 3300 todetermine the subject of the operation. As described with reference toFIGS. 28A and 28B, the wearable system can identify the word “quarter”as the subject of the operation. Thus, the wearable system can changethe subject (“quarter”) to the resulting word “corner”.

FIG. 34 illustrates an example of editing a selected word 3400 using avirtual keyboard 3410. The virtual keyboard 3410 can be controlled byuser gaze inputs, gesture inputs, inputs received from a user inputdevice, etc. For example, a user may type out a replacement word bymoving the eye gaze direction 3420 over the virtual keyboard 3410displayed to the user by the display of the wearable system 200. Theuser may type each letter in the replacement word by pausing their gazeover a respective key for a threshold period of time, or the wearablesystem may recognize changes in direction of the user's gaze 3420 over aparticular key as an indication the user wants to select that key(thereby eliminating the need for the user to hold their focus steady oneach individual key when typing out a word). As described with referenceto FIG. 28D, in certain implementations, the wearable system may varythe size of the cone based on the size of the keys. For example, in avirtual keyboard 3410 where the size of each key is relatively small,the wearable system may reduce the size of the cone to allow a user toidentify the letters in the replacement word more accurately (such thata cone cast will not accidentally capture a large number of possiblekeys). If the size is relatively big, the wearable system canaccordingly increase the size of the keys to so that the user does nothave to pinpoint the gaze direction (which can reduce fatigue).

In certain implementations, after a word has been selected, the wearablesystem can present a set of possible actions in addition to or inalternative to displaying a list of suggested alternative words forreplacing the selected word. The user 210 can select an action and editthe selected word using the techniques described herein. FIG. 35illustrates an example user interface that displays possible actions toapply to a selected word. In FIG. 35, upon selection of a word 3500 forediting, the wearable system may present a list 3510 of options forediting, including (in this example) an option to (1) change the word(using any of the techniques described herein for editing), (2) cut theword out and optionally store it in a clipboard or copy the word andstore it in a clipboard, or (3) paste in a word or phrase from theclipboard. Additional or alternative options that may be presentedinclude a delete selection option, an undo option, a redo option, aselect all option, an insert here option, and a replace option. Thevarious options may be selected using gaze input, totem input, gestureinput, etc. as described herein.

Examples of Interacting with a Phrase with Multimodal User Inputs

While the preceding examples have described using multimodal inputs toselect and edit a word, this is intended for illustration, and the sameor similar processes and inputs may generally be used in selecting andediting a phrase or a sentence or a paragraph including multiple wordsor characters.

FIGS. 36(i)-36(iii) illustrates an example of interacting with a phraseusing multimodal inputs. At FIG. 36(i), the wearable system candetermine the user's gaze 3600 direction, and perform and cone castbased on the user's gaze direction. At FIG. 36(ii), the system mayrecognize that the gaze 3600 of the user 210 is focused on a first word3610 (e.g., “I'm”). The system may make such determinations of the firstword 3610 using any of the techniques discussed herein, including butnot limited to recognizing that the user's gaze 3600 has dwelled (e.g.,lingered) on a particular word for a threshold period of time, that theuser's gaze 3600 is on a particular word at the same time the userprovides voice, gesture, or totem input, etc. The wearable system canalso display a focus indicator (e.g., a contrasting background as shown)on the selected word “I'm” 3610 to indicate that the word has beendetermined from the eye gaze cone cast. The user can actuate a totem3620 (which is an example of the user input device 466) while looking atthe first word 3610. This actuation may indicate that the user intendsto select a phrase or a sentence beginning with the first word 3610.

At FIG. 36(iii), after the actuation of the user input device 466, theuser can look at the last intended word (e.g., the word “there”) toindicate that the user desires to select the phrase starting from theword “I'm” and ending at the word “there”. The wearable system can alsodetect that the user has stopped actuating the totem 3620 (e.g.,releasing the button that the user previously pressed) and canaccordingly select the entire range 3630 of the phrase “I'm flying infrom Boston and will be there”. The system can display the selectedphrase using a focus indicator (e.g., by extending the contrastingbackground to all the words in the phrase).

The system may determine that the user desires to select a phrase ratherthan another word for editing using a variety of techniques. As anexample, the system may determine that the user desires to select aphrase rather than undo their selection of the first word when the userselects a second word shortly after the user selects a first word. Asanother example, the system may determine that the user wants to selecta phrase when the user selects a second word that appears after thefirst and the user has not yet edited the first selected word. As yetanother example, the user may press a button on totem 3620 when they arefocused on first word 3610 and then hold the button until their gaze hassettled on the last word. When the system recognizes the button waspressed while the gaze 3610 was focus on a first word, but only releasedafter the user's gaze 3610 shifted to a second word, the system mayrecognize the multimodal user input as a selection of a phrase. Thesystem may then identify all of the words in the phrase, including thefirst word, the last word, and all words in between and may enableediting of the phrase as a whole. The system may use a focus indicatorto highlight the selected phrase (e.g., highlighting, emphasized text(e.g., bold or italic or a different color), etc.) so that it stands outfrom unselected text. The system may then display contextuallyappropriate options for editing the selected phrase, such as options3510, a virtual keyboard such as keyboard 3410, alternative phrases,etc. The system may receive additional user inputs such as spoken input,totem input, gesture input, etc. to determine how to edit the selectedphrase 3630.

While FIG. 36 illustrates the user selecting a first word 3610 that isat the start of a phrase, the system may also allow a user to selectbackwards from the first word 3610. In other words, the user may selecta phrase by selecting the last word of a phrase (e.g., “there”), andthen by selecting the first word of the desired phrase (e.g., “I'm”).

FIGS. 37A-37B illustrate another example of interacting with a textusing multimodal inputs. In FIG. 37A, a user 210 utters a sentence (“Iwant to sleep”). The wearable system can capture the user's utterance asa speech input 3700. For this speech input, the wearable system candisplay both primary and secondary results from the automated speechrecognition (ASR) engine for each word, as shown in FIG. 37B. Theprimary result for each word may represent the ASR engine's best guess(e.g., the word having the highest ASR score for indicating what wordthe user actually spoke) for the word spoken by the user in speech input3700, whereas the secondary results may represent similarly soundingalternatives or words having lower ASR scores than the ASR engine's bestguess. In this figure FIG. 37B, the primary results are displayed as thesequence 3752. In some embodiments, the wearable system may presentalternative results or hypotheses as alternative phrases and/or entiresentences as opposed to alternative words. As an example, the wearablesystem may provide a primary result of “four score and seven years ago”along with a secondary result of “force caring seven years to go” wherethere is no one-to-one correspondence between discrete words in theprimary and secondary results. In such embodiments, the wearable systemcan support inputs from the user (in any of the manners describedherein) selecting the alternative or secondary phase(s) and/orsentence(s).

As shown in FIG. 37B, each word from the user's speech input 3700 may bedisplayed as a collection 3710, 3720, 3730, 3740 of primary andsecondary results. Arrangements of this type may enable a user toquickly swap out incorrect primary results and correct any errorsintroduced by the ASR engine. The primary results 3752 may be emphasizedwith a focus indicator (e.g., each word is in bold text surrounded by abounding box in the example in FIG. 37B) to distinguish them from thesecondary results.

The user 210 can dwell on secondary results, e.g., secondary words,phrases, or sentences etc., if the primary words are not the onesintended by the user. As an example, the ASR engine's primary result incollection 3740 is “slip,” whereas the correct transcription is actuallythe first secondary result “sleep.” To correct this error, the user canfocus their gaze upon the correct secondary result “sleep” and thesystem may recognize that the user's gaze lingering upon a secondaryresult for a threshold period of time. The system may translate the usergaze input as a request to replace the primary result “slip” with theselected secondary result “sleep.” Additional user inputs may bereceived in conjunction with selecting a desired secondary result, suchas user speech input (e.g., the user may ask the system to “edit”, “usethis”, or “replace” while looking at the desired secondary result).

Once the user finishes editing the phrase “I want to sleep” or confirmsthat the transcription is correct, the phrase can be added to a body oftext using any modes of user input described herein. For example, theuser can say a hotword, such as “finish” to cause the edited phrase tobe added back to a body of text.

Example Processes of Interacting with Text Using a Combination of UserInputs

FIG. 38 is a process flow diagram of an example method 3800 of usingmultiple modes of user input to interact with a text. The process 3800can be performed by the wearable system 200 described herein.

At block 3810, the wearable system may receive spoken input from a user.The speech input may include the user's speech containing one or morewords. In one example, the user may dictate a message and the wearablesystem may receive this dictated message. This may be achieved throughany suitable input device, such as the audio sensor 232.

At block 3820, the wearable system may transform the speech input intotext. The wearable system may utilize an automatic speech recognition(ASR) engine to transform the user's spoken input into text (e.g., aliteral transcription), and may further leverage natural languageprocessing techniques to transform such text into a semanticrepresentation indicative of intents and concepts. The ASR engine may beoptimized for free-form text input.

At block 3830, the wearable system may tokenize the text into discreteactionable elements such as words, phrases, or sentences. The wearablesystem may also display the text for the user, using a display systemsuch as the display 220. In some embodiments, the wearable system doesnot need to understand the meaning of the text during the tokenizationsystem. In other embodiments, the wearable system is equipped with thecapacities to understand the meaning of the text (e.g., one or morenatural language processing models or other probabilistic statisticalmodels), or simply the capacities to distinguish between (i) words,phrases, and sentences that represent a user-composed message or portionthereof, and (ii) words, phrases, and sentences that do not represent auser-composed message or portion thereof, but instead correspond tocommands to be executed by the wearable system. For example, thewearable system may need to know the meaning of the text for recognizinga command operation or a parameter of the command spoken by the user.Examples of such text may include context-specific wakeup words thatserve to invoke one or more constrained system command librariesassociated with editing for each of one or more different user inputmodalities, which are also referred to herein as hotwords.

A user can interact with one or more of the actionable elements usingmultimodal user inputs. At block 3840, the wearable system can selectone or more elements in response to a first indication. The firstindication can be one user input or a combination of user inputs asdescribed herein. The wearable system may receive input from the userselecting one or more of the elements of the text string for editing.The user may select a single word or multiple words (e.g., a phrase or asentence). The wearable system may receive a user input selectingelement(s) for editing in any desired form including, but not limitedto, a speech input, a gaze input (e.g., via the inward-facing imagingsystem 462), a gesture input (e.g., as captured by the outward-facingimaging system 464), a totem input (e.g., via actuation of a user inputdevice 466), or any combinations thereof. As examples, the wearablesystem may receive a user input in the form of a user's gaze lingeringon a particular word for a threshold period of time or may receive auser's gaze on a particular word at the same time as a user input via amicrophone or totem indicating a selection of that particular word forediting.

At block 3850, the wearable system can edit the selected element(s) inresponse to a second indication. The second indication can be receivedvia a single mode of input or a combination of input modes as describedwith preceding figures including, but not limited to, user gaze input,spoken input, gesture input, and totem input. The wearable system mayreceive user input indicating how the selected element(s) should beedited. The wearable system may edit the selected element(s) accordingto the user input received in block 3850. For example, the wearablesystem can replace the selected element based on a speech input. Thewearable system can also present a list of suggested alternatives andchoose among the selected alternatives based on the user's eye gaze. Thewearable system can also receive input via user interactions with avirtual keyboard or via a user input device 466 (such as, e.g., aphysical keyboard or a handheld device).

At block 3860, the wearable system can display a result of editing theselected element(s). In certain implementations, the wearable system canprovide a focus indicator on the element(s) that is edited.

As indicated by the arrow 3870, the wearable system may repeat blocks3840, 3850, and 3860 if the user provides additional user input to editadditional element(s) of the text.

Additional details relating to multimodal task execution and textediting for wearable systems are provided in U.S. patent applicationSer. No. 15/955,204, filed Apr. 17, 2018, titled MULTIMODAL TASKEXECUTION AND TEXT EDITING FOR A WEARABLE SYSTEM, published as U.S. Pat.Pub. No. 2018/0307303, which is hereby incorporated by reference hereinin its entirety.

Examples of Transmodal Input Fusion Techniques

As described above, transmodal input fusion techniques providing dynamicselection of appropriate input modes can advantageously permit a user tomore accurately and confidently target real or virtual objects and canprovide a more robust, user-friendly AR/MR/VR experience.

A wearable system can advantageously support opportunistic fusion ofmultiple modes of user input to facilitate user interactions in athree-dimensional (3D) environment. The system can detect when a user isproviding two or more inputs, via two or more respective input modes,that may converge together. As an example, a user may be pointing to avirtual object with their finger, while also directing their eye gaze atthe virtual object. The wearable system may detect this convergence ofthe eye gaze and finger gesture inputs and apply an opportunistic fusionof the eye gaze and finger gesture inputs and thereby determine, withgreater accuracy and/or speed, which virtual object the user is pointingto. The system thus allows the user to select smaller elements (or morerapidly moving elements) by reducing the uncertainty of the primaryinput targeting method. The system can also be used to speed up andsimplify the selection of elements. The system can allow the user toimprove the success of targeting moving elements. The system can be usedto speed up rich rendering of display elements. The system can be usedto prioritize and speed up the local (and cloud) processing of objectpoint cloud data, dense meshing and plane acquisition to improve theindentation fidelity of found entities and surfaces, along with graspedobjects of interest. Embodiments of the transmodal techniques describedherein allows the system to establish varying degrees of transmodalfocus from the user's point of view, while still preserving smallmotions of the head, eye and hands, thereby greatly enhancing thesystem's understanding of user intent.

As will further be described herein, identification of a transmodalstate may be performed through an analysis of the relative convergenceof some or all of the available input vectors. For example, this may beachieved by examining the angular distance between pairs of targetingvectors (e.g., a vector from the user's eyes to the target object and avector from a totem held by a user pointing toward the target object).Then, the relative variance of each pair of inputs may be examined. Ifthe distances or variances are below thresholds, then a bimodal state(e.g., a bimodal input convergence) can be associated with a pair ofinputs. If a triplet of inputs has targeting vectors with angulardistances or variances below thresholds, then a trimodal state can beassociated with the triplet of inputs. Convergence of four or moreinputs is also possible. In the example of a head pose targeting vector(head gaze), an eye vergence targeting vector (eye gaze), and a trackedcontroller or tracked hand (hand pointer) targeting vector triplet isidentified, the triplet may be referred to as a transmodal triangle. Therelative size of the triangle sides, area of this triangle, or itsassociated variances may present characteristic traits that the systemcan use to predict targeting and activation intent. For example, if thearea of the transmodal triangle is less than a threshold area, then atrimodal state can be associated with the triplet of inputs. Examples ofvergence calculations are provided herein and in Appendix A. As anexample of predictive targeting, the system may recognize that a user'seye and head inputs tend to converge prior to convergence of the user'shand input. For example, when trying to grasp an object, eye and headmovements can be performed quickly and may converge onto the object ashort time (e.g., about 200 ms) beforehand movement input converges. Thesystem can detect the head-eye convergence and predict that the handmovement input will soon thereafter converge.

The convergence of targeting vector pairs (bimodal), triplets(trimodal), or quartets (quadmodal), or higher numbers of inputs (e.g.,5, 6, 7, or more) can be used to further define the subtype oftransmodal coordination. In at least some embodiments, a desired fusionmethod is identified based on the detailed transmodal state of thesystem. The desired fusion method may also be determined, at least inparty, by the transmodal type (e.g., which inputs are converged), themotion type (e.g., how the converged inputs are moving), and theinteraction field type (e.g., in which interaction field, such as themid-field region, taskspace, and workspace interactive regions describedin connection with FIGS. 44A, 44B, and 44C the inputs are focused). Inat least some embodiments, the selected fusion method may determinewhich of the available input modes (and associated input vectors) areselectively converged. The motion type and field type may determinesettings of the selected fusion method, such as the relative weightingor filtering of one or more of the converged inputs.

Additional benefits and examples of techniques related to transmodalinputs for interacting with virtual objects and the opportunistic fusionof multiple modes of user input are further described with reference toFIGS. 39A-60B (as well as below and in Appendix A).

Explanation of Certain Transmodal Terminology

Explanations of certain terms used for transmodal input fusiontechniques are provided below. These explanations are intended toillustrate, but not to limit, the scope of transmodal terminology.Transmodal terminology is to be understood from the perspective of aperson of ordinary skill in the art in view of the entirety of thedescription set forth in the specification, claims, and accompanyingfigures.

The term IP region can include a volume associate with an interactionpoint (IP). For example, a pinch IP region can include a volume (e.g.,spherical) created by the posed separation of the index fingertip andthe thumb-tip. The term region of intent (ROI) can include a volumeconstructed from overlapping uncertainty regions (e.g., volumes)associated with a set of targeting vectors for an intended targetobject. The ROI can represent the volume in which the intended targetobject is likely to be found.

The term modal input can refer to input from any of the sensors of thewearable system. For example, common modal inputs include inputs fromsix degree of freedom (6DOF) sensors (e.g., for head pose or totemposition or orientation), eye-tracking cameras, microphones (e.g., forvoice commands), outward-facing cameras (for hand or body gestures),etc. Transmodal input can refer to simultaneous use of multiple,dynamically coupled modal inputs.

The term vergence can include the convergence of multiple input vectorsassociated with multiple modes of user input on a common interactionpoint (e.g., when an eye gaze and a hand gesture both point to the samespatial location). The term fixation can include the localized slowingand pause of a vergence point or a point of a single input vector. Theterm dwell can include a fixation that extends in time for a least agiven duration. The term ballistic pursuit can include a ballistic(e.g., projectile like) motion of a vergence point towards a target orother object. The term smooth pursuit can include the smooth (e.g., lowacceleration or low jerk) motion of a vergence point towards a target orother object.

The term sensor convergence can include the convergence of sensor data(e.g., the convergence of data from a gyroscope with the data from anaccelerometer forming an inertial measurement unit (IMU), theconvergence of data from an IMU with a camera, the convergence ofmultiple cameras for a SLAM process, etc.). The term feature convergencecan include the spatial convergence of inputs (e.g., the convergence ofinput vectors from multiple modes of input) as well as the temporalconvergence of inputs (e.g., the concurrent or sequential timing ofmultiple modes of input).

The term bimodal convergence can include the convergence of two inputmodes (e.g., the convergence of two input modes as the input vectorsconverge on a common interaction point). The term trimodal and quadmodalconvergence can respectively include the convergence of three and fourinput modes. The term transmodal convergence can include the transientconvergence of multiple inputs (e.g., the detection of a temporaryconvergence of multiple modes of user input and correspondingintegration or fusion of those user inputs to improve the overall inputexperience).

The term divergence can refer to at least one input mode that waspreviously converged (and resultingly fused) with at least one otherinput mode and that is no longer converged with that other input mode(e.g., a trimodal divergence may refer to a transition from a trimodalconvergence state to a bimodal convergence state as aninitially-convergent, third input vector diverges from converged firstand second input vectors).

The term head-hand-vergence can include the convergence of the head andhand raycast vectors or the convergence of the head pose and handinteraction points. The term head-eye vergence can include theconvergence of the head pose and eye gaze vectors. The termhead-eye-hand vergence can include the convergence of the head pose, eyegaze, and hand direction input vectors.

The term passive transmodal intent can include pre-targeting, targeting,head-eye fixation and dwell. The term active transmodal intent caninclude head-eye-hand dwell interaction, or head-eye-hand manipulationinteraction. The term transmodal triangle can include an area created bythe convergence of three modal input vectors (and may refer to the areaof uncertainty of a trimodal converged input). This area may also bereferred to as a vergence area or modal vergence area. The termtransmodal quadrangle can include an area created by the convergence offour modal input vectors.

Examples of User Inputs

FIGS. 39A and 39B illustrate examples of user inputs received throughcontroller buttons or input regions on a user input device. Inparticular, FIGS. 39A and 39B illustrates that a controller 3900, whichmay be a part of the wearable system disclosed herein and which mayinclude a home button 3902, trigger 3904, bumper 3906, and touchpad3908. The user input device 466 or the totem 1516 described withreference to FIGS. 4 and 15, respectively, can serve as controllers 3900in various embodiments of wearable systems 200.

Potential user inputs that can be received through controller 3900include, but are not limited to, pressing and releasing the home button3902; half and full (and other partial) pressing of the trigger 3904;releasing the trigger 3904; pressing and releasing the bumper 3906;touching, moving while touching, releasing a touch, increasing ordecreasing pressure on a touch, touching a specific portion such as anedge of the touchpad 3908, or making a gesture on the touchpad 3908(e.g., by drawing a shape with the thumb).

FIG. 39C illustrates examples of user inputs received through physicalmovement of a controller or a head-mounted device (HMD). As shown inFIG. 39C, physical movement of controller 3900 and of a head mounteddisplay 3910 (HMD) may form user inputs into the system. The HMD 3910can comprise the head-worn components 220, 230 shown in FIG. 2A or thehead mounted wearable component 58 shown in FIG. 2B. In someembodiments, the controller 3900 provides three degree-of-freedom (3DOF) input, by recognizing rotation of controller 3900 in any direction.In other embodiments, the controller 3900 provides six degree-of-freedom(6 DOF) input, by also recognizing translation of the controller in anydirection. In still other embodiments, the controller 3900 may provideless than 6 DOF or less than 3 DOF input. Similarly, the head mounteddisplay 3910 may recognize and receive 3 DOF, 6 DOF, less than 6 DOF, orless than 3 DOF input.

FIG. 39D illustrates examples of how user inputs may have differentdurations. As shown in FIG. 39D, certain user inputs may have a shortduration (e.g., a duration of less than a fraction of a second, such as0.25 seconds) or may have a long duration (e.g., a duration of more thana fraction of a second, such as more than 0.25 seconds). In at leastsome embodiments, the duration of an input may itself be recognized andutilized by the system as an input. Short and long duration inputs canbe treated differently by the wearable system 200. For example, a shortduration input may represent selection of an object, whereas a longduration input may represent activation of the object (e.g., causingexecution of an app associated with the object).

FIGS. 40A, 40B, 41A, 41B, and 41C illustrate various examples of userinputs that may be received and recognized by the system. The userinputs may be received over one or more modes of user input(individually, or in combination, as illustrated). The user inputs mayinclude inputs through controller buttons such as home button 3902,trigger 3904, bumper 3906, and touchpad 3908; physical movement ofcontroller 3900 or HMD 3910; eye gaze direction; head pose direction;gestures; voice inputs; etc.

As shown in FIG. 40A a short press and release of the home button 3902may indicate a home tap action, whereas a long press of the home button3902 may indicate a home press & hold action. Similarly, a short pressand release of the trigger 3904 or bumper 3906 may indicate a triggertap action or a bumper tap action, respectively; while a long press ofthe trigger 3904 or bumper 3906 may indicate a trigger press & holdaction or a bumper press & hold action, respectively.

As shown in FIG. 40B, a touch of the touchpad 3908 that moves over thetouchpad may indicate a touch drag action. A short touch and release ofthe touchpad 3908, where the touch doesn't move substantially, mayindicate a light tap action. If such a short touch and release oftouchpad 3908 is done with more than some threshold level of force(which may be a predetermined threshold, a dynamically determinedthreshold, a learned threshold, or some combination thereof), the inputmay indicate a force tap input. A touch of the touchpad 3908 with morethan the threshold level of force may indicate a force press action,while a long touch with such force may indicate a force press and holdinput. A touch near the edge of the touchpad 3908 may indicate an edgepress action. In some embodiments, an edge press action may also involvean edge touch of more than a threshold level of pressure. FIG. 40B alsoshows that a touch on touchpad 3908 that moves in an arc may indicate atouch circle action.

The examples of FIG. 41A illustrate that interaction with the touchpad3908 (e.g., by moving the thumb over the touchpad) or physical movement(6 DOF) of controller 3900 can be used to rotate virtual objects (e.g.,by making a circular gesture on the touchpad), move virtual objects inthe z-direction toward or away from the user (e.g., by making a gestureon the touchpad in, say, the y-direction), and grow or shrink the sizeof a virtual object (e.g., by making a gesture in a different directionon the touchpad, say, the x-direction).

FIG. 41A also shows that combinations of inputs can represent actions.In particular, FIG. 41 illustrates that interaction with bumper 3906 anda user turning and tilting their head (e.g., adjusting their head-pose)can indicate a manipulate start and/or manipulate end action. As anexample, a user may provide an indication to start manipulation of anobject by double tapping or pressing and holding the bumper 3906, maythen move the object by providing additional inputs, and then mayprovide an indication to end manipulation of the object by doubletapping or releasing the bumper 3906. In at least some embodiments, theuser may provide additional inputs to move the object in the form ofphysical movement (6 or 3 DOF) of the controller 3900 or by adjustingtheir head pose (e.g., tilting and/or rotating their head).

FIG. 41B illustrates additional examples of user interactions. In atleast some embodiments, the interactions of FIG. 41B involvingtwo-dimensional (2D) content. In still other embodiments, theinteractions of FIG. 41B may be used for three-dimensional content. Asshown in FIG. 41B, a head pose (which may be directed to the 2D content)combined with a moving touch on the touchpad 3908 may indicate a setselection action or a scroll action. A head pose combined with a presswith force on the edge of touchpad 3908 may indicate a scroll action. Ahead pose combined with a light and short tap on the touchpad 3908 orcombined with a short tap with pressure on the touchpad 3908 mayindicate an active action. Pressing with force and holding the forcefulpress on touchpad 3908 combined with a head-pose (which may be aparticular head pose) may indicate a context menu action.

As shown in FIG. 41C, the wearable device can use head pose indicatingthe user's head is pointing toward a virtual app together with a hometap action to open a menu associated with the app or use head posetogether with a home press & hold action to open a launcher application(e.g., an app that permits executing multiple apps). In someembodiments, the wearable device can open a launcher applicationassociated with a pre-targeted application using a home tap action(e.g., a single or double tap of the home button 3902).

FIGS. 42A, 42B, and 42C illustrate examples of user inputs in the formof fine finger gestures and hand movements. The user inputs illustratedin FIGS. 42A, 42B, and 42C may sometimes be referred to herein asmicrogestures and may take the form of fine finger movements such aspinching a thumb and index finger together, pointing with a singlefinger, grabbing with an closing or opening hand, pointing with a thumb,tapping with a thumb, etc. The microgestures may be detected by thewearable system using a camera system, as one example. In particular,the microgestures may be detected using one or more cameras (which mayinclude a pair of cameras in a stereo configuration), which may be apart of the outward-facing imaging system 464 (shown in FIG. 4). Objectrecognizers 708 can analyze imagery from the outward-facing imagingsystem 464 to recognize the example microgestures shown in FIGS.42A-42C. In some implementations, microgestures are activated by thesystem when the system determines the user has focused on a targetobject for a sufficiently long fixation or dwell time (e.g., convergenceof multiple input modes is considered robust).

Examples of Perceptive Fields, Display Render Planes, and InteractiveRegions

FIG. 43A illustrates a visual perceptive field and an auditoryperceptive field of the wearable system. As shown in FIG. 43A, a usermay have a primary field of view (FOV) and peripheral FOV in theirvisual field. Similarly, a user may sense direction in an auditoryperceptive field having at least forward, rearward, and peripheraldirections.

FIG. 43B illustrates display render planes of a wearable system havingmultiple depth planes. In the example of FIG. 43B, the wearable systemhas at least two render planes, one displaying virtual content at adepth of about 1.0 meter and another displaying virtual content at adepth of about 3.0 meters. The wearable system may display virtualcontent at a given virtual depth on the depth plane having the closestdisplay depth. FIG. 43B also illustrates a 50 degree field of view forthis example wearable system. Additionally, FIG. 43B illustrates a nearclipping plane at approximately 0.3 meters and a far clipping plane atapproximately 4.0 meters. Virtual content that is closer than the nearclipping plane may be clipped (e.g., not displayed) or may be shiftedaway from the user (e.g., to at least the distance of the near clippingplane). Similarly, virtual content that is further from the user thanthe far clipping plane may be clipped or may be shifted towards the user(e.g., to at least the distance of the far clipping plane).

FIGS. 44A, 44B, and 44C illustrate examples of different interactiveregions around a user, including a mid-field region, an extendedworkspace, a workspace, a taskspace, a manipulation space, an inspectionspace, and a head space. These interactive regions represent spatialregions in which the user can interact with real and virtual objects,and the type of interaction may be different in different regions, andthe appropriate set of sensors used for transmodal fusion may bedifferent in the different regions. For example, the workspace caninclude a region in front of the user, within the user's field of view(FOV), and within the user's reach (e.g., out to about 0.7 m). Thetaskspace may be a volume within the workspace and may generallycorrespond to the volume in which users are comfortable manipulatingobjects with their hands (e.g., from about 0.2 to 0.65 m). The taskspacemay include angles (measured from a forward level vector) that aregenerally downward, e.g., from about 20 degrees below level to about 45degrees below level. The inspection space may be a volume within thetaskspace and may generally correspond to the volume in which user's mayhold an object when inspecting it closely (e.g., from about 0.2 to 0.3m). The type of inputs in the inspection space, the taskspace, and theworkspace typically can include head pose, eye gaze, hand gestures(e.g., the microgestures illustrated in FIGS. 42A-42C). At largerdistances from the user, the extended workspace may extend to a distanceof about 1 m, the workspace may be a volume in front of the usergenerally within the user's FOV, while the mid-field region mayspherically to a distance of about 4 meters (from the user's head). Thehead space may correspond to the volume occupied by a user's head (e.g.,out to the near clipping plane shown in FIG. 43B) and any head-mountedcomponents of the wearable system disclosed herein.

The near field region includes the regions near to the user and extendsfrom about 0.7 m to about 1.2 m from the user. The midfield regionextends beyond the near field and out to about 4 m. A farfield regionextends beyond the midfield region and can include distances out to thedistance of the greatest render plane or greatest depth plane providedby the wearable system (which can be up to 10 m or 30 m or even toinfinity). In some implementations, the midfield region can range fromabout 1.2 m to about 2.5 m and may represent the region of space inwhich a user can “lean in” and grab or interact with real or virtualobjects. In some such implementations, the farfield region extendsbeyond about 2.5 m.

The example interactive regions shown in FIGS. 44A-44C are illustrativeand not limiting. User interactive regions can be arranged differentlythan shown and can have different sizes, shapes, etc.

Degrees of Input Integration

As will be further described below, the development of transmodal inputfusion techniques represents increasing input integration, fromstatically defined input systems through dynamic coupling based on inputfeedback systems to input coupling that operates with multiple dynamicfeedback and feedforward systems (e.g., to dynamically anticipate orpredict the input sensors to use). For example, unimodal techniques mayutilize a single, sensor with exclusive controls (e.g., a touchgesture), and multimodal techniques may utilize multiple independentsensors operating in concurrently, in parallel (e.g., head gaze and abutton selection on an input device). Crossmodal techniques can utilizemultiple sensors that are statically fused (e.g., permanentlycross-coupled). In techniques such as these, the wearable systemtypically accepts all the sensor inputs and determines the likely intentof the user (e.g., to select a particular object).

In contrast, transmodal input fusion techniques provide for dynamiccoupling of sensor input, e.g., identifying sensor inputs that haveconverged spatially (e.g., converged in a spatial region around a targetobject) or temporally (e.g., for a fixation or dwell time).

In some implementations, transmodally coupled sensor input occurs arelatively small fraction of the time the user is interacting with the3D environment. For example, in some such implementations, transmodallycoupled input occurs only about 2% of the time. However, during the timethat an appropriate set of converged sensors are identified, transmodalinput fusion techniques can substantially increase the accuracy oftarget object selection and interaction.

Unimodal User Interactions

FIG. 45 illustrates an example of a unimodal user interaction. Inunimodal user interactions, a user input is received via a single modeof user input. In the example of FIG. 45, a touch input is registered asa touch gesture, and no other modes of input are used in interpretingthe user input. The touch input may, if desired, include changes in thesingle mode of user input over time (e.g., a touch that moves, followedby a tap).

Multimodal User Interactions

FIGS. 46A, 46B, 46C, 46D, and 46E illustrate examples of multimodal userinteractions. In multimodal user interactions, independent modes of userinput are received and leveraged together to improve the userexperience. However, in multimodal interactions, dynamic fusing ofdifferent sensor modalities typically does not occur. In figures similarto FIGS. 46A and 46B, the user actions are shown on the left (e.g., theuser can provide touch input or move his or her head, eye(s), orhand(s)) and the corresponding input control (e.g., touch gesture, headgaze (also referred to as head pose), eye gaze, and hand gesture).

As shown in the example of FIGS. 46A and 46B, a user may target avirtual object using their head gaze and then select the virtual objectusing a controller button. Thus, the user provides input over two modes(head gaze and button), and the system leverages both to determine thatthe user desires to select the virtual object the user's head ispointing at.

Additionally, the modes of user input that are combined in multimodaluser interactions may be somewhat interchangeable. As an example, theuser may target a virtual object using their eye gaze, instead of theirhead pose. As another example, the user may select the virtual targetusing a blink, hand gesture, or other input, rather than a controllerbutton.

FIG. 46C illustrates examples of multimodal user interactions in thenear-field and mid-field interactive regions, which may correspond tothe workspace and mid-field of FIG. 44B, as an example. As shown in FIG.46C, a user 4610 may target a virtual object 4600 in the near-fieldregion using a totem collider directly associated with the position atotem 4602, a totem touchpad cursor (e.g., by manipulating a touch padto move a mouse arrow over the virtual object 4600), or by making aparticular gesture 4604 on or near the virtual object 4600. Similarly, auser 4610 may target a virtual object 4612 in the mid-field region usinghead pose, eye gaze pose, a controller 4602 having 3 or 6 DOF inputs, orusing a hand or arm gesture 4604. In the examples of FIG. 46C, theinteractions are multimodal due to the fact that there are multipleunimodal options available for a user to provide the same input to thesystem.

While FIG. 46C illustrates examples of multimodal user interactionsassociated with targeting, FIG. 46D illustrates examples of multimodaluser interactions associated with targeting and selection (in thenear-field and mid-field interactive regions). As shown in FIG. 46D, auser 4610 may target the virtual objects 4600 and 4612 selected in FIG.46C using various techniques including, but not limited to, pressing abumper or trigger on a totem (also referred to herein as a controller),pressing or tapping a touchpad on the totem, performing a microgesturesuch as a finger pinch or tap, or lingering (e.g., dwelling) the inputused to target the object (e.g., keeping their head pose, eye pose,totem projection, or hand gesture focused on the virtual object 4600 or4612 for longer than some threshold amount. In the examples of FIG. 46D,the interactions are multimodal due to the fact that a user is usingmultiple modes of input (where time itself, in the form of a dwell, maybe a mode of input).

As illustrated in FIG. 46E, the user may select the targeted virtualobjects, such as objects 4600 and 4612, using a mode of input differentfrom that used for targeting. As shown in the various examples of FIG.46E, a user may select a targeted virtual object using varioustechniques including, but not limited to, pressing a trigger or bumperor tapping on a totem touchpad (e.g., while using a head pose, eye pose,or totem for targeting) and making a hand or microgesture such as apinch tap, a tap gesture, or a pointing gesture (e.g., while using ahand targeting gesture). In the examples of FIG. 46E, the interactionsare multimodal due to the fact that a user is using multiple modes ofinput.

Crossmodal User Interactions

FIGS. 47A, 47B, and 47C illustrate examples of crossmodal userinteractions. In crossmodal user interactions, a first mode of userinput is modified by a second mode of user input. In the example oftargeting, a primary mode of user input may be used to target a desiredvirtual object and a secondary mode of user input may be used to adjustthe primary mode of user input. This may be referred to as a relativecursor mode. As an example of a relative cursor mode, a user may provideprimary control of a cursor using a first mode of user input (such aseye gaze) and may adjust the position of the cursor using a second modeof user input (such as input on a controller). This may provide the userwith more accurate control over the cursor.

As shown in FIG. 47C, a head ray cast (e.g., head pose) may be receivedthat roughly targets a virtual object 4700. Then, inputs from a totemmay be received that impart a delta to the head ray case in order tofine-tune the targeting of the virtual object 4700. Once the user issatisfied, the user may provide a touch tap to select the targetedvirtual object 4700. FIG. 47C also illustrates various examples ofsimilar processes, using different combinations of primary and secondarymodes of user input and different examples of selection inputs.

Transmodal User Interactions

FIGS. 48A, 48B, and 49 illustrate various examples of transmodal userinteractions. In transmodal user interactions, two or more modes of userinput may be dynamically coupled together. As an example, the wearablesystem may dynamically detect when two (or more) different modes of userinput are convergent and may then combine the inputs received over thosemodes to achieve a better result than any individual input couldotherwise provide.

As shown in the example of FIG. 48A, a user's hand gesture inputs, headpose inputs, and eye gaze inputs can be dynamically integrated and splitas the inputs converge together and split apart.

At time 4800, a user is providing hand gesture input to target aparticular virtual object. As an example, the user may be pointing atthe virtual object.

At time 4802, the user has focused their eyes on the same virtual object(as the user targeted with their hand(s)) and has also turned their headto point at the same virtual object. Thus, all three modes of input(hand gesture, eye pose, and head pose) have converged together on acommon virtual object. The wearable system may detect this convergence(e.g., trimodal convergence) and enable filtering of the user inputs toreduce any uncertainty associated with the user input (e.g., to increasethe probability that the system correctly identifies the virtual objectthat the user is intending to select). The system can selectivelyutilize different filters and/or models of user input to processconvergent inputs (e.g., based on factors such as which inputs haveconverged and how strongly the inputs have converged). As an example,the system may overlay or otherwise combine the uncertainty regions foreach of the converged inputs and thereby determine a new uncertaintyregion that is smaller than the individual uncertainties. In at leastsome embodiments, the wearable system may integrate converged inputswith different weights given to different user inputs. As an example,eye gaze inputs may more accurately indicate the current location of thetarget, and thus given more weight, than head or hand pose inputs overthis brief period of time. In particular, even when using eye trackingsystems with relatively low resolution cameras and sampling rates, auser's eye tends to lead other inputs and therefore tends to respondquicker to small changes in the location of a target. As such, the eyegaze inputs may provide a more accurate input vector when conditionedproperly (e.g., filtered and fused with other inputs in a suitableweighted manner).

At time 4804, the user has diverted their eye gaze away from the virtualobject, while their head pose and hand gesture remain focused orpointing at the virtual object. In other words, the user's eye gaze hasdiverged and is no longer converged with the user's head pose and handgesture inputs. The wearable system may detect this divergence event andadjust its filtering of the different user inputs accordingly. As anexample, the wearable system may continue to combine the user's headpose and hand gesture inputs (e.g., in a bimodal convergence) toidentify which virtual object the user wishes to select, while ignoringthe eye gaze input for that purpose.

At time 4806, the user has returned their eye gaze to the virtual objectsuch that the user's eye gaze and hand gesture are converged on thevirtual object. The wearable system can detect this bimodal convergenceand combine or fuse the two inputs in a weighted manner.

As shown in the example of FIG. 48B, transmodal selection or targetingof objects may include dynamically cross-coupled inputs and may be usedfor static objects and ballistic objects (e.g., moving objects) invarious interactive regions such as the near-field (sometimes referredto as taskspace) and mid-field regions. FIG. 48B illustrates that a usermay target static or moving objects using transmodal inputs. As anexample, a user may target an object by turning their head and eye gazeto an object (and potentially lingering such inputs on the object and/ortracking the object with such inputs). The user may also provideadditional inputs such as controller or hand gestures that select theobject (e.g., that converge with the head and/or eye gaze to form atrimodal input).

As shown in the example of FIG. 49, the system may dynamically crosscouple inputs together. At time 4900, the system may fuse together eyegaze inputs with head pose inputs, allowing the user to look to selectan object (while using their head pose to increase the accuracy of theireye gaze input). At time 4910, the system may fuse together head pose,eye pose, and hand gesture inputs, thereby allowing the user to look andpoint to select an object (while using the head and eye gaze pose inputsto increase the accuracy of the hand gesture input).

Example Processes of Transmodal Interactions

FIG. 50 is a process flow diagram of an example method 5000 of usingmultiple modes of user input to interact with a wearable system. Theprocess 5000 can be performed by the wearable system 200 describedherein.

At block 5002, the wearable system may receive user inputs and classifythe modal interaction and determine occurrence of any convergences ofdifferent modes of user inputs. The wearable system may classify themodal interaction as being bimodal or trimodal (e.g., having two orthree different user input modes converged together) or quadmodal (e.g.,four input modes) or a higher number of input modes. Block 5002 mayinvolve detection and classification of various phases of a modalinteraction such as union or the initial formation of a bimodal ortrimodal “bond” (e.g., where the difference between the input vectors ordifferent user input modes falls below some given threshold), settlingor the stabilization of the bimodal or trimodal “bond” (e.g., where thedifference between the input vectors stabilizes below the threshold),and divergence (e.g., where the difference between the input vectorsincreases beyond the threshold). In some cases, two input modes maystabilize before an action is performed or before another input modeconverges with them. For example, head and eye input may converge andstabilize a short time (e.g., about 200 ms) before a hand graspingaction is performed or beforehand input converges with the head and eyeinput.

Block 5002 may also involve classifying the motion type and interactionregion of a modal interaction. In particular, block 5002 may involvedetermining if the converged user inputs are in ballistic pursuit (e.g.,have a variable velocity or variable acceleration), in smooth pursuit(e.g., have a more constant velocity with low acceleration), in fixation(e.g., have a relatively low velocity and acceleration over time), or indwell (e.g., have been in fixation for more than a given amount oftime). Additionally, block 5002 may involve determining if the convergedinputs are within a near-field region (e.g., a taskfield region), amid-field region, or a far-field region. The system may processtransmodal inputs differently depending on the motion type andinteraction region.

At block 5004, the wearable system may apply filtering to transmodalinputs. Block 5004 may involve applying filtering of different strengthsbased on how strong the vergence between the inputs is, which inputs areconverged together, which region the inputs are convergent in, etc. Asan example, when an eye pose input and a hand gesture input areconverged and in the mid or far-field regions, it may be necessary toapply stronger filtering than when such inputs are in the near-fieldregions (due to the generally increasing uncertainty of these inputswith greater distance from the user). In at least some embodiments,filtering in block 5004 may involve conditioning of inputs and may ormay not include removing portions of the inputs. As an example, thesystem may filter transmodal inputs by noise filtering a primary inputpose, to increase targeting accuracy. Such filtering may include alow-pass filter, such as a one Euro filter, that filters out highfrequency components (and which may include an adaptive cutofffrequency). While such filtering can increase targeting accuracy even inthe absence of fusion, leaving such filtering on permanently (as opposedto dynamically while inputs are converged) can result in theintroduction of noticeable (and undesirable) latency. By selectivelyapplying noise filtering only when inputs are converged (which mayrepresent a small fraction of operational time), the system can retainthe accuracy benefit of applying a low-pass filter while avoiding thevast majority of noticeable latency. In various implementations, otherfilters can be used (alone or in combination) including, e.g., a Kalmanfilter, a finite impulse response (FIR) filter, an infinite impulseresponse (IIR) filter, a moving average, a single or double exponential,etc. Another example of a filter is a dynamic recursive low pass filterin which a low pass filter has a dynamically adjustable cutoff frequencysuch that at low speeds of the input vectors, the cutoff frequency issmaller to reduce jitter (while allowing a small degree of lag or timelatency) and at high speeds of the input vectors, the cutoff frequencyis larger to reduce lag as compared to jitter.

At block 5006, the wearable system may integrate any user inputs thatare converged. In combining the user inputs, the wearable system mayinterpolate (linearly, spherically, or otherwise) between the userinputs to create a combined or fused input. In some embodiments, thewearable system may perform easing (linear, quadratic, exponential, orotherwise) to avoid jittery inputs. In particular, the wearable systemmay smooth out sudden changes in the combined input. As an example, atthe moment the difference between two inputs becomes smaller than thethreshold for convergence, the active input of the wearable system mayjump from an active one of the inputs to the new fused input. To avoidjitter, the wearable system may move the active input (and anycorresponding cursor or feedback mechanism) from the original activeinput to the new fused input in a damped manner (e.g., with a finiteacceleration away from the original active input, travel, then a finitedeceleration to the new fused input). The actions in block 5006 can bedynamic in that the method 5000 can continuously or repeatedly check forconverged or diverged inputs and integrate the converged inputs. Forexample, the method can dynamically integrate an input that hasconverged and dynamically remove an input that has diverged.

At block 5008, the wearable system may optionally provide feedback tothe user that user inputs have been fused and transmodal interactionsare now available. As examples, the wearable system may provide suchfeedback in the form of text, a visual marker such as a point, line,ring, arc, triangle (e.g., for trimodal convergence), square (e.g., forquadmodal convergence), and a mesh).

Examples of User Selection Processes in Unimodal, Bi-Modal, andTri-Modal Interactions

FIG. 51 illustrates examples of user selections in unimodal, bi-modal,and tri-modal interactions.

In unimodal interaction 5100, a user is providing a user input to trackor select a given object via a first mode of user input. The mode ofuser input in interaction 5100 may be any suitable mode such as a headpose, eye pose, hand gesture, controller or totem input, etc. The userinput in interaction 5100 is generally an input from a user identifyinga particular area or volume for selection. As illustrated in FIG. 51,there may be an uncertainty associated with the user input ininteraction 5100. In particular, user inputs generally have at leastsome amount of uncertainty due to the limits of input devices such aseye tracking systems, head pose tracking systems, gesture trackingsystems, etc. In at least some embodiments, this uncertainty maydecrease over time (e.g., as the system averages an otherwise constantuser input over time). This effect is illustrated in FIG. 51 by circles,which represent uncertainty of a given user input and which shrink overtime (e.g., compare the relatively large uncertainties at time 5102versus the relatively small uncertainties at time 5104).

In bimodal interaction 5110, a user is providing user input via twodifferent modes of user input to track or select a given object. Themodes of user input in interaction 5110 may be any suitable combinationof modes such as head-eye, hand-eye, head-hand, etc. As shown in FIG.51, the system may use the overlapping uncertainties to decrease thesystem's uncertainty of the user interaction. As an example, the systemmay identify the region 5112 that lies within both the uncertainty areaof the first mode of user input and the uncertainty area of the secondmode of user input in bimodal interaction 5110. As illustrated in FIG.51, the overlapping error region 5112 associated with the fused bimodalinput is substantially smaller than the error region associated with anyone of the constitution modal inputs. Additionally, the overlappingerror region 5112 may also shrink over time, as the uncertainty of eachof the underlying modal inputs decreases (e.g., the bimodal uncertainty5112 may generally decrease from an initial time 5102 to a later time5104).

In trimodal interaction 5120, a user is providing user input via threedifferent modes of user input to track or select a given object. Themodes of user input in interaction 5110 may be any suitable combinationof modes such as head-eye-hand, head-eye-totem, etc. As discussed inconnection with bimodal interaction 5110, the system may use theoverlapping uncertainties of the three different modes to decrease thesystem's overall uncertainty of the user interaction. As an example, thesystem may identify the region 5122 that lies within the uncertaintyareas of the first, second, and third modes of user input. The overalluncertainty region 5122 may sometimes be referred to as a transmodaltriangle (for tri-modal interactions) and a transmodal quadrangle (forquad-modal interactions) and a transmodal polygon (for higher numbers ofinput interactions). As shown in FIG. 51, the overall uncertainty region5122 may also shrink over time, as the uncertainty of each of theunderlying modal inputs decreases (e.g., the trimodal uncertainty 5122may generally decrease from an initial time 5102 to a later time 5104).FIG. 51 also illustrates, in interaction 5121, an overall uncertaintyregion 5123 in the form of a circle rather than a triangle. The exactshape of uncertainty regions formed from multiple converged modes ofuser input may depend on the shape of uncertainty regions of theunderlying user inputs.

In at least some embodiments, the lengths of the “legs” of thetransmodal triangle, transmodal quadrangle, and/or a higher-leveltransmodal shape, may be proportional to the degree of convergence ofeach pair of input pose vectors associated with a respective leg. Thelengths of the “legs” may also be indicative of the type of task and/orcharacteristic to individual users (e.g., different users may tend tointeract in different and identifiable ways that may be reflected in thelengths of the “legs”). In various embodiments, the lengths of the“legs” or sides of a transmodal shape may be used in classifying whattype of user interaction is involved. For example, the area of thetriangle or quadrangle (depending on number of input poses) is directlyproportional to transmodal convergence and can be applied to a broadrange of scenarios. In use, two different triangles may have the samearea but different side lengths. In this case, the length of the sidesmay be used to classify the subtype of input convergence, and the areamay be a proxy for intensity of intent along with the variance of thearea.

Example of Interpreting User Input Based on Convergence of User Inputs

In at least some embodiments, the system disclosed herein may interpretuser inputs based on convergence of user inputs. In the example of FIG.52, a user is providing various user inputs to select an object 5200.The user has turned their head towards object 5200, thus providing headpose input 5202; the user is looking at the object 5200, therebyproviding eye gaze input 5204; and the user is gesturing with their arm.

In general, it may be difficult for the system to interpret the user'sarm gesture as it potentially has several different meanings. Perhapsthe user is pointing at the object 5200, such that the raycast from theuser's wrist to their palm (or fingertip) represents the intended input.Alternatively, perhaps the user is forming an “0” with their fingers andmoving their hand such that the “0” surrounds the object in their lineof line of sight, such that the raycast from the user's head to theirfingers represents the intended input. In yet another alternative,perhaps the user is pointing a water hose at a car and intends for theraycast from their shoulder to their fingertip to represent the intendedinput. Absent additional information, the system may struggle todetermine which is the intended input.

With the present system, the system may determine which of the potentialinputs (e.g., one of the potential interpretations of the arm or handgesture input) are converged with inputs of another mode (e.g., the heador eye gaze pose inputs). The system may then assume that the potentialinterpretation that results in modal convergence is the intended input.In the example of FIG. 52, the palm-fingertip input 5206 (e.g.,wrist-palm, joint-fingertip, etc.) is converged with the head and eyegaze inputs, while the head-palm (e.g., head-fingertip) input 5208 andthe shoulder-palm (e.g., shoulder-fingertip) input 5210 are divergentwith the other inputs. Thus, the system may determine that thepalm-fingertip input 5206 is most likely the intended input and may thenuse the palm-fingertip input 5206 in identifying the object 5200 forselection (e.g., by using the input 5206 and its uncertaintycharacteristics to reduce the overall uncertainty characteristics forthe trimodal selection of object 5200). Thus, the system may interpretthe arm gesture based at least in part on which possible interpretationsresult in modal convergences.

FIGS. 53, 54, 55, and 56 illustrate additional examples of how userinput may be interpreted based on convergence of user inputs.

FIG. 53 illustrates how a trimodal convergence, of a head pose (H), eyegaze pose (E), and a palm-fingertip input (Ha, for hand), may occur ineither a near-field region 5300 (which may be a taskspace or inspectionspace of the type shown in FIGS. 44A, 44B, and 44C), a mid-field region5302 (which may correspond to the mid-field region from between about 1and 4 meters from the user shown in FIGS. 44A, 44B, and 44C), and afar-field region 5304 (which may correspond to regions beyond themid-field region of FIGS. 44A, 44B, and 44C). As shown in the examplesof FIG. 53, the H, H, and Ha inputs may converge together at points5301, 5303, 5305, and 5307. The Ha input may involve fingertip positionin the example associated with convergence point 5301, whereas the Hainput may involve the palm-to-fingertip raycast in the examplesassociated with convergence points 5303, 5305, and 5307.

In at least some embodiments, a system having transmodal fusioncapabilities may place different weights (e.g., importance values) ondifferent inputs. In general, inputs may be dominant (e.g., be assignedmore weight) when those inputs have factors such as lower error,increased functionality (such as providing depth information), higherfrequency, etc. FIGS. 54, 55, and 56 illustrate how systems havingtransmodal fusion capabilities may utilize the differing weights ofinputs in interpreting user inputs. As an example and in at least someembodiments, eye gaze (E) inputs may be dominant over head pose (H)inputs and hand gesture (Ha), as the eye gaze inputs include anindication of depth (e.g., distance from the user), whereas head poseinputs don't include an indication of depth, and as eye gaze (E) maysometimes be more indicative of user input that hand gestures (Ha).

As shown in the examples of FIG. 54, eye gaze (E) inputs may determinehow a system having transmodal fusion capabilities interprets variousother inputs. In the examples of FIG. 54, eye gaze (E) inputs and headpose (H) inputs appear to converge at points 5400, 5402, and 5404 (whichmay be in the near-field, mid-field, and far-field regions,respectively). However, the head pose (H) inputs also appear to convergewith the hand gesture (Ha) inputs at points 5401, 5403, and 5405 (whichmay be substantially far away as to not be on the sheet of FIG. 54),which are beyond the convergence points of the E and H inputs. Thesystem described herein may decide to ignore the apparent convergencesat points 5401, 5403, and 5405 and instead utilize the apparentconvergences at points 5400, 5402, and 5404. The system may do so basedon the relatively higher weight of eye gaze (E) inputs.

In other words, FIG. 54 shows how inputs such as head pose (H) may havemultiple interpretations (e.g., one fusing with eye gaze (E) and anotherfusing with hand gesture (Ha)) and how the system may selectinterpretations that result in convergences with higher weight inputs.Thus, FIG. 54 illustrates how the system may ignore apparent inputconvergences when the convergence is inconsistent with another, moredominant, input such as eye gaze (E) input.

FIG. 55 shows examples similar to FIG. 54, except that the eye gaze (E)inputs converge with the hand gesture (Ha) inputs at points 5500, 5502,and 5504. In addition, the head pose (H) inputs apparently converge withthe Ha inputs at points 5501, 5503, and 5505. As with FIG. 54, thesystem may decide in the examples of FIG. 55 to prefer the apparentconvergences involving the more-dominant eye gaze (E) inputs.

In the examples of FIG. 56, the eye gaze (E) inputs diverge from boththe hand gesture (Ha) inputs and the head pose (H) inputs. As such, thesystem may decide to utilize the apparent convergences of the handgesture (Ha) inputs and the head pose (H) inputs (e.g., the inputs topoints 5600, 5602, 5604, and 5606) as the intended transmodal inputs.The system may filter out the eye gaze (E) inputs to points 5601, 5603,5605, and 5607 or may utilize the eye gaze (E) inputs for other uses.

Example Diagrams of a Wearable System having Transmodal FusionCapabilities

FIG. 57A is a block system diagram of an example processing architectureof a wearable system 200 that fuses multiple modes of user input tofacilitate user interactions (e.g., a wearable system with transmodalcapabilities). The processing architecture may be implemented by thelocal processing and data module 260 shown in FIG. 2A or the processor128 shown in FIG. 2B. As shown in FIG. 57A, the processing architecturemay include one or more interaction blocks such as blocks 5706 and 5708that implement transmodal fusion techniques of the type describedherein. Block 5706, for example, receives inputs such as head pose, eyegaze direction, and hand gesture inputs and can apply transmodal fusiontechniques (such as filtering and combining the inputs when inputconvergence is detected) to those inputs. Similarly, block 5708 receivesinputs such as controller input, voice input, head pose input, eyeinput, and hand gesture input and can apply transmodal fusion techniquesto these (and any other available) user inputs. The integrated andfiltered user inputs can, as illustrated by the arrows of FIG. 57A, bepassed along to and used by various software applications.

FIG. 57B is a block system diagram of another example of a processingarchitecture of a wearable system 200 with transmodal inputcapabilities. FIG. 57B shows how the processing architecture can includea transmodal interaction toolkit 5752 as part of a software developmentkit (SDK), thus allowing software developers to selectively implementsome or all of the available transmodal input capabilities of thesystem.

Example Graphs of Vergence Distances and Area for Converged andDivergent User Interactions

FIGS. 58A and 58B are graphs of observed vergence distances of variousinput pairs and of the vergence area for user interactions with thewearable system, where a user was asked to track a static object usingtheir head, eyes, and a controller. In the example of FIG. 58A, dynamictransmodal input fusion was disabled, while in the example of FIG. 58Bdynamic transmodal input fusion was enabled. In the example of FIG. 58A,the user was presented with a static object at time 5810 at a firstlocation, and then again at time 5820 at a second location. In theexample of FIG. 58B, the user was presented with a static object at time5830 at a first location, and then again at time 5840 at a secondlocation.

FIGS. 58A and 58B show the changes over time of the head-eye vergencedistance 5800, the head-controller vergence distance 5802, and thecontroller-eye vergence distance 5804. The head-eye vergence distance5800 is the distance between the head pose input vector and the eye gazeinput vector. Similarly, the head-controller vergence distance 5802 isthe distance between the head pose input vector and a controller inputvector. Additionally, the controller-eye vergence distance 5804 is thedistance between the controller input vector and the eye gaze inputvector. FIGS. 58A and 58B also graph the vergence area 5806, which maybe indicative of the area of uncertainty associated with the user inputs(e.g., the uncertainty the system has in connection with the user'sinput tracking the object).

As shown in FIG. 58B (particularly when contrasted with FIG. 58A),transmodal filtering of the head pose and eye gaze input vectorssignificantly reduces the vergence area 5806 and the head-eye vergencedistance 5800. In particular and after an initial spike as the usershifts their inputs to a newly-presented object at times 5830 and 5840,the head-eye vergence distance 5800 is drastically reduced by dynamictransmodal filtering. In the example of FIG. 58B, dynamic transmodalfiltering may include identifying that the head pose and eye gaze inputshave converged and then integrating the inputs together and applyingfiltering to achieve a more accurate result than either input couldachieve on its own.

The system can use information similar to the graphs in FIGS. 58A and58B to determine a user's cognitive load (e.g., the effort being used inthe working memory of the user). For example, the rates of rise of thegraphs 5800-5804 or the temporal differences between the peaks of thesecurves represent the user's mental processing power that can be appliedto the task. For example, if the cognitive load is low, the user candevote more working memory to the task, and the rise times may besteeper, and the peaks more closely spaced in time, because the user hassufficient cognitive load to accomplish the task (e.g., targeting amoving object). If the cognitive load is high, the user has less workingmemory to apply to the task, and the rise times may be less steep andthe peaks spaced out longer in time, because the user takes longer toaccomplish the task. Note that the rise time (to the peak) for eyeinputs tends to be quicker than that for head input, and both tend to bequicker than for hand inputs (e.g., the head-controller graph 5802 canbe seen in FIGS. 58A, 58B to have a less steep rise and a delayed peakrelative to the head-eye graph 5800).

An Example of Dwelling and Feedback

FIGS. 59A and 59B illustrate an example of user interaction and feedbackduring a fixation and dwell event. In particular, FIGS. 59A and 59B showhow a user may provide input by shifting their eye gaze input onto anobject 5910, fixate on the object 5910, and dwell (e.g., linger) theirgaze on the object 5910 for a threshold period of time. Graphs 5900 and5901 show the rate of change (e.g., speed) of the user's eye gaze inputover time.

At time 5902, the user finished shifting their eye gaze onto the object5910. If desired, the system may provide feedback to the user. Thefeedback may take the form of indicator 5911 and may indicate that theobject 5910 is at least temporarily selected.

After the user's gaze has dwelled on the object 5910 for an initialthreshold period (represented by time 5904), the system may providefeedback 5912 to the user. In some cases, the dwell time 5904 for an eyeon an object is about 500 ms. The feedback 5912 may be in the form of aprogress bar, a progress arc, or other such mechanism to show roughlywhat percentage of dwell time has been completed to successfully providea dwelling user input. As one example, feedback 5914 may take the formof a continuously or progressively updated fixation arc. In at leastsome embodiments, the fixation arc 5914 may be stepped closer tocompletion as the user's eye gaze fixations for incrementally longerlengths of time (e.g., as indicated by the vertical dashed lines ingraph 5900.

After the user's gaze has dwelled on the object 5910 for a thresholdperiod (represented by time 5906), the system may provide feedback 5914to the user. The feedback 5914 may be in any desired form such as acompleted progress bar, a completed progress arc, highlighting of theobject, etc. In the illustrated example of FIG. 59B, feedback 5914 is inthe form of a completed square surrounding object 5910.

Although this is example is based on eye gaze, the concepts areapplicable to other sensor input modalities, e.g., head pose, handinput, etc., alone or in combination with other inputs. For example, thedwell time 5904, 5906 for either eye gaze or head pose toward an objectmay be about 500 ms, but if both eye gaze and head pose inputs convergeon the object, the dwell time might reduce to 300 ms for the system todetermine the user has selected the object, given the greater certaintyin targeting due to the convergence of these two input modes.

Examples of Personalization for Transmodal Input Fusion Techniques

A wearable system can monitor a user's interaction with the 3Denvironment and how the sensors tend to converge or diverge during use.The system can apply machine learning techniques to learn the user'sbehavior patterns and convergence/divergence tendencies. For example, auser may have unsteady hands (e.g., due to genetic effects, age, ordisease) and therefore there may be more jitter associated with handinputs, because the user's hand tends to shake during totem usage. Thesystem can learn this behavior and adjust thresholds (e.g., increasingthe variance threshold to determine convergence of hand input with otherinput(s)) or apply suitable filtering to compensate for the user's handjitter (e.g., by adjusting a cutoff frequency in a low pass filter). Fora user with more steady hands, the thresholds or filters can be setdifferently by the system, since the hand sensor input for that userwould display less jitter. Continuing with this example, the system canlearn how a particular user picks up or grasps objects, by learning thesequence and timing of sensor convergences (or divergences) that areparticular to that user (see, e.g., the time sequences ofhead-eye-controller vergences in FIG. 58B).

Accordingly, the system can provide an improved or maximal userexperience for any particular user by adaptively integrating theappropriate set of converged inputs for that particular user. Suchpersonalization may be beneficial for users with poor coordination,illness, age, etc. by permitting the system to customize thresholds andfilters to recognize input convergence (or divergence) more readily.

Transmodal input techniques can permit a wearable system to operate moreefficiently. The wearable system can include numerous sensors (see,e.g., the sensors described with reference to FIG. 16) including bothinward-facing and outward-facing cameras, depth cameras, IMUs,microphones, a user input device (e.g., a totem), electromagnetictracking sensors, ultrasonic, radar, or laser sensors, electromyogram(EMG) sensors, etc. The system may have processing threads that trackeach sensor input. The sensor processing threads may updated at a lowerrate until a convergence event is identified, and then the sensor updaterate (at least for converged inputs) can be increased, which improvesefficiency by having higher update rates only for the converged sensorinputs. As described above, the system can learn user behavior andpredict which sensor input(s) may converge based on a time history ofother sensor convergences. For example, convergence of head and eyeinputs onto a target object may indicate the user is attempting to graspor interact with the target object, and the system can accordinglypredict that hand input will converge soon thereafter (e.g., a fewhundred milliseconds or up to a second or so later in time). The systemcan increase the hand sensor thread update rate based on thisprediction, prior to the hand sensor input actually converging. Suchpredictive abilities can provide the user with zero or little perceivedlatency in the response of the wearable system, because the converged—orsoon to be converged—sensors have increased update rates, reducinglatency or lag in the system.

As another example, the system can initiate (or wake up) certainprocessing routines based on the convergence (or not) of sensor inputs.For example, realistically rendering a virtual diamond in the user'senvironment may require processor-intensive subsurface scattering (orsubsurface light transport) techniques to be applied to convey thesparkle of the virtual diamond. Performing suchcomputationally-intensive tasks every time a user glances at or near thevirtual diamond can be inefficient. Therefore, by detecting a spatialconvergence (e.g., of the user's head, eye, and hand inputs) or atemporal convergence (e.g., eye fixation on the diamond for greater thana dwell time), the system can perform the computationally-intensivesubsurface rendering techniques only when convergence indicates the useris interacting with the virtual diamond (e.g., by looking at it for along time, reaching out to grasp it, etc. This can lead to improvedefficiency, because these processing routines are only executed whennecessary. Although described for a subsurface scattering or lighttransport technique, other computationally-intensive augmentationtechniques can additionally or alternatively be applied such as, e.g.,reflection maps, surface shimmer effects, subsurface scattering, gaseouslensing and refraction, particle counts, advanced high dynamic range(HDR) rendering or lighting methods, etc.

As yet another example, the user's head may be turning toward an object.The system can predict that the user's head will be pointed at theobject at a particular time in the future, and the system can beginrendering (or preparations for rendering) virtual objects so that whenthe user's head has arrived at the object at the future time, the systemwill be able to render the virtual object(s) with little or no perceivedlatency.

User history, behavior, personalization, etc. can be stored as acomponent of the world map or model 920, e.g., locally or remotely(e.g., in the cloud). The system may start with a default set oftransmodal parameters that are successively improved as the systemlearns and adapts to user behavior. For example, the world map or model920 may include a transmodal interaction profile for the user withinformation on thresholds, variances, filters, and so forth that areparticular to that user's way of interacting with the real or virtualworld. Transmodal input fusion is user-centric in which the appropriateset of sensor inputs that have converged—for that particular user—areintegrated together to provide improved targeting, tracking, andinteractions.

Example Wearable Systems with Electromyogram (EMG) Sensors

In some embodiments, fine motor control may be enabled by additionalsensors, such as depicted in FIGS. 60A and 60B that provide positionalfeedback or additional sensor input. For example, an electromyogram(EMG) sensor system as described herein may provide controller orgesture data, which may serve as an additional or alternative input in atransmodal input system and may facilitate precise selection and userinput. Like other input modes described herein, inputs received throughthe EMG system may be opportunistically fused with other input modes,when the inputs converge, thereby improving the speed or accuracy of theEMG inputs and any other fused inputs. Accordingly, EMG sensor input canbe utilized with any of the transmodal, multimodal, or cross modaltechniques described herein. EMG sensor is a broad term as used hereinand may include any type of sensor configured to detect neuromuscularactivity or neuromuscular signals (e.g., neural activation of spinalmotor neurons to innervate a muscle, muscle activation, or musclecontraction). EMG sensors can include mechanomyography (MMG) sensors,sonomyography (SMG) sensors, etc. EMG sensors may include an electrodeconfigured to measure electric potentials on the surface or inside thebody, a vibration sensor configured to measure skin surface vibrations,an acoustic sensor configured to measure acoustic (e.g., ultrasound)signals arising from muscular activity, etc.

Referring to FIGS. 60A and 60B, additional embodiments are illustratedwherein electromyogram, or EMG, technologies may be utilized to assistin determining the position of one or more portions of the user's body,such as the positions of one or more of the fingers or thumb of the userand/or the positions of one or more of the hands of the user, when theuser is operating a wearable computing system. An example of a wearablecomputing system is depicted in FIG. 60A, comprising a head mountedmodule 58, a handheld controller module 606, and a belt pack or basemodule 70 (modules which are further described herein, e.g., at least inconnection with FIG. 2B), each of which may be operatively coupled 6028,6032, 6034, 6036, 6038, 6042, such as via wired or wireless connectivityconfiguration (such as IEEE 802-11 connectivity configurations,Bluetooth wireless configurations, and the like), to each other, and toother connected resources (46, such as, e.g., cloud resources, which mayalso be referred to as computing resources for storage and/orprocessing).

EMG technologies can be utilized in various sensor configurations, suchas, e.g., an in-dwelling electrode, or a surface electrode, to monitorthe activation of muscles or muscle groups. With modern manufacturingand connectivity advancements, EMG electrodes may be utilized to formsystems or aspects of systems which are unconventional relative toprevious uses. Referring again to FIG. 60A, a cuff or other couplingplatform (6026) may be utilized to couple one or more EMG electrodesagainst a portion of the body, such as the forearm proximal of the hand(6000). The EMG electrodes may be operatively coupled (6020, 6022, 6024;such as via direct wire lead or wireless connectivity) to a localcontroller module (6018), which may be configured to have on-boardpower, such as via a battery, a controller or processor, and variousamplifiers to assist in reducing signal to noise ratio in observinginformation generated from associated EMG electrodes (in the depictedembodiment, three arrays, 6012, 6014, and 6016 of non-indwelling EMGsurface electrodes are illustrated; however this is for illustration andis not a limitation). The EMG-related signals may be passed by the localcontroller module (6018), or directly from the electrodes (6012, 6014,6016) themselves if so connected, to other modules (70, 606, 58, 46) ofthe operatively coupled system utilizing wired or wireless connectivity(6044, 6040, 6030, 6046) configurations, such as, e.g., IEEE 802-11connectivity configurations, Bluetooth wireless configurations, and thelike.

Referring again to FIG. 60A, the EMG electrodes may be placed relativeto the user's anatomy, so that they may be utilized to track theactivation of various muscles known to produce various movements in therelated joints, such as by tracking the muscles that pass through thecarpal tunnel of the wrist to move the various joints of the hand, toproduce, for example, hand movements such as gestures. In variousconfigurations, the EMG electrodes can be placed on or in a user'sanatomy such as, e.g., on a finger, an arm, a leg, a foot, the neck orhead, the torso, and so forth. Multiple EMG electrodes can be placed onor in the user's anatomy so as to detect muscular signals from multiplemuscles or muscle groups. The configuration shown in FIGS. 60A and 60Bis intended to be illustrative and not limiting.

In one embodiment, with all of the illustrated modules operativelycoupled to each other, a central processor, which may reside, forexample in the belt pack or base module 70 or on the cloud 46 may beutilized to coordinate and refine the tracking of hand 6000 gestures,which may be visible by camera elements (such as, e.g., world camera(s)124 of the head mounted wearable 58 of FIG. 2B) of the head mountedmodule 58. The gestures may also be tracked in certain embodiments byfeatures of the handheld controller module 70, which also may featurecertain camera elements (such as world camera(s) 124 of the hand heldcomponent 606 of FIG. 2B) which may be capable of capturing informationregarding various aspects of the hand 6000 position, depending upon theposition/orientation of the various camera elements and theposition/orientation of the pertinent hand 6000 of the user. In otherwords, in one embodiment, EMG data predictive of hand motions may beutilized alone or along with camera-based data pertaining toobservations of the hand, to assist in refining the system's predictionof where the hand is in space relative to the various other modules, andwhat the various portions of the hand are doing.

For example, one or more camera views may be utilized to provide aprediction that the user is making the American nonverbal “OK” symbolwith his or her thumb and index finger (an example of an “OK” sign isshown in FIG. 42C). The EMG data associated with the various musclespassing through the carpal tunnel of the user may be observed to furtherthe understanding that, indeed, the thumb and index finger of the userseem to be flexed in a manner commonly correlated with making of theAmerican nonverbal “OK” symbol—and thus the system is able to provide amore accurate prediction regarding what the user is doing. Thisperception of “OK” may be fed back into the system to provide anindication from the user that whatever next step, dialog box, or thelike, is accepted within the software which may be operated by the useras he or she wears the various wearable computing modules 70, 606, 58,6010—wearable EMG module. In one variation, for example, at a givenoperational juncture the associated software may present the user with adialog box asking the user to select “OK” or “reject”; this dialog boxmay be observed by the user through the head mounted module 58 in anaugmented reality or virtual reality visualization mode, for example Inthis illustrative example, to select the “OK” button in the software,the user may produce the “OK” symbol with his hands, as described above.The three arrays may be utilized to assist with common mode errorrejection in refining the EMG module output, and/or may be utilized toobserve different muscles or portions thereof, to assist in theobservation of what is going un underneath the user's skin that islikely to be correlated with a movement of, for example, the hand of theuser.

Although the foregoing example is described in the context of handgestures (and in particular an “OK” symbol), this is for illustrationand is not a limitation of the EMG sensor system. As described above,EMG electrodes can be placed on or in the user's anatomy to measuresignals from other muscle groups so as to determine that the user ismaking any form of gesture (e.g., a nonverbal symbol). Examples ofgestures (or nonverbal symbols) have been described with reference tothe gestures 2080 of FIG. 20 or the gestures described with reference toFIGS. 42A-42C. Thus, the EMG system can measure gestures, nonverbalsymbols, positions, or movements by fingers, arms, feet, legs, the torso(e.g., twisting or bending), the neck or head, and so forth.

Referring to FIG. 60B, for illustrative purposes, an embodiment similarto that of FIG. 60A is depicted, but without an interconnected handheldmodule 606, as such handheld module may not be needed or desired forcertain system configurations or functional paradigms.

Additional Examples of Transmodal Input Fusion Techniques

This section provides additional details regarding examples of variousimplementations of transmodal input fusion. Transmodal input fusion mayprovide opportunistic feature fusion of multimodal input usingegocentric motion dynamics to improve interaction fidelity. Theseexample implementations are intended to be illustrative and notlimiting. These techniques can be performed by the wearable displaysystems described elsewhere in this application (see, e.g., the wearablesystems 200 described with reference to FIGS. 2A and 2B). Any particularwearable system may implement one, some, or all of these functionalitiesand techniques or may implement additional of different functionalitiesand techniques.

The following provides an explanation of some of the terms used fortransmodal fusion techniques described herein. These explanations areintended to be illustrative and not to be limiting:

Elements: discrete interactive display items.

Primary Targeting Vector: dominant input pose vector used to steerspatial targeting methods (e.g., raycast, conecast, ballcast, hittestsor normals associated with a collider)

Input Pose Vector: A pose obtained from standard system modal inputs.This can be a head gaze pose, eye gaze pose, controller pose or handpose. It can also be from crossmodal statically blended inputs thatcreate statically fused pose vectors such as controller and touchpad oreye gaze and touchpad derived poses.

Interaction Field: This can be based the effective reach of the user andconstrained by overlapping limits from fields of sensing, fields ofdisplay and audio fields.

Region of Intent (ROI): This can be a volume constructed from theoverlapping uncertainty regions (volumes) associated with the set oftargeting vector pairs or triplets.

Identification of the transmodal state: The identification of thetransmodal state can be performed through the analysis of the relativeconvergence of all defined and available input targeting vectors. Thiscan be achieved by first examining the angular distance between pairs oftargeting vectors. Then the relative variance of each pair is examined.If the distances and variances are below defined thresholds then abimodal state can be associated with a pair. If a triplet targetingvectors have a set of angular distances and variances below definedthresholds then an associated area can be established. In the case of ahead pose targeting vector (head gaze), eye vergence targeting vector(eye gaze) and tracked controller or tracked hand (hand pointer)targeting vector triplet is created that match these requirements it iscalled a transmodal triangle. The relative size of the triangle sides,area of this triangle and its associated variances presentcharacteristic traits the can be used to predict targeting andactivation intent. The group motion of targeting vector pairs (bimodal),or triplets (trimodal) (or large groups of sensor inputs, e.g., 4, 5, 6,7, or more) can be used to further define the exact subtype oftransmodal coordination.

Selection and configuration of fusion methods: The appropriate fusionmethod can be identified based on the detailed transmodal state of theinput system. This can be defined by the transmodal type, the motiontype, and the interaction field type. The defined fusion type determineswhich of the listed input modes (and associated input vectors) should beselectively fused. The motion type and field type determine the fusionmethod settings.

The techniques described herein can allow the user to select smallerelements by reducing the uncertainty of the primary input targetingmethod. The techniques described herein can also be used to speed up andsimplify the selection of elements. The techniques described herein canallow the user to improve the success of targeting moving elements. Thetechniques described herein can be used to speed up rich rendering ofdisplay elements. The techniques described herein can be used toprioritize and speed up the local (and cloud) processing of object pointcloud data, dense meshing and plane acquisition to improve theindentation fidelity of found entities and surfaces, along with graspedobjects of interest.

The techniques described herein allow, for example, a wearable system200, to establish varying degrees of multimodal focus from the user'spoint of view while still preserving small motions of the head, eye andhands, greatly enhancing the system understanding of user intent.

Examples of some of the functionality that the transmodal input fusiontechniques can address are described below. This functionality includes,but is not limited to: Targeting small elements; Rapidly targetingstatic close proximity elements; Targeting dynamically moving elements;Managing the transition between nearfield and midfield targetingmethods; Managing the transition between relative targeting methods;Managing the activation of elements; Managing the manipulation ofelements; Managing the transition between macro and micro manipulationmethods; Managing the deactivation reintegration of elements; Managingthe active physical modeling elements; Managing the active rendering ofdisplay elements; Managing the active acquisition of dense point clouds;Managing the active acquisition of dense meshes; Managing activeacquisition of planar surfaces; Managing active acquisition of dynamicfound entities; and Managing active modeling of grasped found entities.

(1) Targeting small elements: This technique can provide for targetingsmall elements (discrete interactive display items). Targeting smallitems at a distance may be inherently difficult. As the presented sizeof the target item decreases to within the limit of accuracy it maybecome increasingly difficult to reliably intersect with a projectedtargeting mechanism. Opportunistically fusing multimodal inputs can actto increase effective accuracy by decreasing uncertainty in the primarytargeting vector. Embodiments of the wearable system 200 can perform anycombination of the following actions: Identifies the current transmodalstate; Identifies the ROI defined by transmodal vergence and fixation;Identifies the corresponding interaction field (near/mid/far); Selectsthe correct input fusion method and settings for the(static/pseudo-static/dynamic) primary targeting vector; Applies definedconditioning to the primary targeting vector; Communicates stabilizedpose vector with increased confidence to application; Reduces targetingvector registration error, jitter and drift; and Enables confidenttargeting of smaller elements near or far (compared to modal targetingmethods).

(2) Rapidly targeting static close proximity elements: This techniquecan provide for reliably targeting static elements that are in closeproximity. Objects that are in close proximity can be inherentlydifficult to resolve and reliably target because the separation distancecan be below the accuracy of the primary input vector. Using theconditioned input pose vector to target static, close proximity targetsprovides an improvement in accuracy with limited change in perceivablelatency. Embodiments of the wearable system 200 can perform anycombination of the following actions: Identifies the current transmodalstate; Identifies the ROI defined by transmodal vergence and fixation;Identifies the corresponding interaction field (near/mid/far); Selectsthe correct input fusion methods for the (pseudo-static) primarytargeting vector; Applies defined conditioning to the primary targetingvector; Presents stabilized pose vector with increased confidence toapplication; and Reduces time to fixation and dwell.

(3) Targeting dynamically moving elements: This technique can providefor reliably targeting moving elements. This includes translating,rotating or scaling relative to the display or relative to the world.Moving objects can be inherently difficult to pursue when in a dynamicenvironment. Such targeting can be more challenging when presented withthe increased number of degrees of freedom offered by world distributed,dynamic 3D content and head and eye driven display methods. Embodimentsof the wearable system 200 can perform any combination of the followingactions: Identifies the ROI defined by transmodal vergence and fixation;Identifies the corresponding interaction field (near/mid/far); Selectsthe correct input fusion methods for the (dynamic) primary targetingvector; Communicates stabilized pose vector with increased confidence toapplication; Reduces targeting vector registration error, jitter anddrift; and Enables confident targeting of smaller elements near or far,moving at greater speeds with nonlinear velocities (compared to modaltargeting methods).

(4) Managing the transition between nearfield and midfield targetingmethods: This technique provides for marshaling between near fieldtargeting mechanics and midfield-farfield targeting methods. There aremultiple methods for direct and indirect targeting methods. Typicallydirect methods occur in the near field and indirect methods targetcontent in the midfield and farfield. Understanding if the region ofintent (ROI) is in the near field or the midfield allows for selectionbetween groups of methods it also presents an opportunity and method foridentifying field transition events and handling associated transitionsin interaction mechanics to reduce the need to explicitly manage modechanges in an application layer. Embodiments of the wearable system 200can perform any combination of the following actions for activetransmodal intent: Identifies the ROI defined by transmodal vergence andfixation; Identifies the corresponding interaction field (near/mid/far);Selects the correct input fusion methods for the (dynamic) primarytargeting vector; and Presents stabilized pose vector increasedconfidence to application.

(5) Managing the transition between relative targeting methods: Thistechnique can provide for confidently selecting the best or most likelyinteraction mechanic within an interaction field. Embodiments of thewearable system 200 can perform any combination of the following actionsfor active transmodal intent: Identifies the current transmodal state;Identifies the ROI defined by transmodal vergence and fixation;Identifies the corresponding interaction field (near/mid/far); Selectsthe most appropriate targeting vector beginning and end point for theprimary input vector; and Manages any transition between targetingvectors (on same mode or between modes). May enforce a pre-targeting“cool down” period between targeting feedback in order to reduce anydisorientation induced from target vector changes in the field of view(FOV).

(6) Managing the activation of elements: Embodiments of the wearablesystem 200 can perform any combination of the following actions foractive transmodal intent: Identifies the current transmodal state;Identifies the ROI defined by transmodal vergence and fixation;Identifies the corresponding interaction field (near/mid/far); Presentsstabilized pose vector increased confidence to application; and Enablesconfident activation of smaller elements near or far, moving at greaterspeeds with nonlinear velocities (compared to modal targeting methods).

(7) Managing the manipulation of elements: Embodiments of the wearablesystem 200 can perform any combination of the following actions:Identifies the current transmodal state; Identifies the ROI defined bytransmodal vergence and fixation; Identifies the correspondinginteraction field (near/mid/far); Presents stabilized pose vectorincreased confidence to application; Enables confident steering andmanipulation of smaller elements near or far, moving at greater speedswith nonlinear velocities (compared to modal targeting methods); Enablesconfident micro-steering and manipulation of elements; and Enablesconfident micro-steering actions.

(8) Managing the transition macro and micro manipulation methods:Embodiments of the wearable system 200 can perform any combination ofthe following actions for active transmodal intent: Identifies the ROIdefined by transmodal vergence and fixation; Identifies thecorresponding interaction field (near/mid/far); Identifies the relevantmodal macro interaction mechanic, checks to see if augmentation isenabled; Activates the relevant micro-interaction management method;Identifies if transmodal divergence has occurred; and Prepares thesystem for the deactivation of operating microgestures by reducingtransmodal confidence.

For example: Hand Gesture Pinch. Can be actively augmented withmicro-pinch manipulations such as thumb_index_joint tap orindex_thumb_slide actions, but these methods may be enabled only when inrobust tracking regions, speeds and confidences along with a robustmeasure of user focus (e.g., fixation or dwell time exceeding a userfocus threshold time of, e.g., a few hundred to a few thousand ms).Transmodal fixation serves as a more robust method to conditionallyactivate micro-gesture analysis.

(9) Managing the deactivation and integration of elements: Embodimentsof the wearable system 200 can perform any combination of the followingactions for active transmodal intent: Identifies the current transmodalstate; Identifies the ROI defined by transmodal vergence and fixation;Identifies the corresponding interaction field (near/mid/far); Presentsstabilized pose vector increased confidence to application; and Enablesconfident micro-motions that can lead to more robust modal input statechanges.

For example: greater confidence for transitions from pinch_touch topinch_hover to pinch_end lead to more predictable virtual object releaseand detachment behaviors.

For example: greater confidence of hand trajectories and posetransitions when partially occluded can lead to better deactivationrecognition and present more reliable end state properties. This canlead to more reliable detachment behaviors, more robust throw mechanicsand more realistic physical behavior.

(10) Managing the physical modeling of elements: Embodiments of thewearable system 200 can perform any combination of the following actionsfor active transmodal intent: Identifies the current transmodal state;Identifies the ROI defined by transmodal vergence and fixation;Identifies the corresponding interaction field (near/mid/far); Presentsstabilized pose vector increased confidence to application; Enablesconfident custom treatment of local conditions resulting from standard(e.g., simulated) physical interactions in ROI that can lead to morerobust state changes in the physics engines; and Enables efficientmanagement and context driven optimization of advanced (e.g., simulated)physical behaviors such as hyperlocal soft body simulation inelasticcollisions or liquid simulation.

For example: greater confidence of resulting behavior of thrown orplaced objects presents a more predictable and intentional physicalbehavior.

(11) Managing the active rendering of display elements: Embodiments ofthe wearable system 200 can perform any combination of the followingactions for passive transmodal intent: Identifies transmodal fixationpoint, defines an extended region of intent based on transmodal fixationtime and predicted dwell time; Identifies if region of intent intersectswith a rendering element and augmentation options; Identifies availableaugmentation that are compatible with ROI, fixation time and predicteddwell time; Activates optimal rendering augmentation options (that mayrequire second and third rendering pass) such as level of detail (LOD)methods that can be activated during predicted dwell time; and Detectstransmodal divergence and manages rendering priority. Activating suchLOD methods may, for example, include increasing the resolution orquality of virtual content or other graphics that are displayed orotherwise presented to a user. Similarly, in some implementations,activating such LOD methods may include increasing the quality of audiothat is output to a user.

For example: detailed reflection maps, surface shimmer effects,subsurface scattering, gaseous lensing and refraction, particle counts,advanced high dynamic range (HDR) rendering or lighting methods, etc.These proposed mechanics can be different from typical foveatedrendering techniques driven by eye vergence locations and tend to usefirst order/first pass rendering methods to manage polygon count, pixeldensity and typically must work on different time scales in order toremain imperceptible. Using transmodal fixation and dwell allows for theinclusion of higher latency rendering options that are typically notattempted on mobile platforms due to compute constraints.

(12) Managing active acquisition of dense point clouds: Embodiments ofthe wearable system 200 can perform any combination of the followingactions for passive transmodal intent: Identifies the ROI defined bytransmodal vergence and fixation; Identifies the correspondinginteraction field (near/mid/far); Accelerate the processing point cloudsof interest; and improves object interaction fidelity by preparing fordirect observation and interaction.

For example object bound dense point clouds for found entities for richelement interaction on surface of found entity (dynamic objecttextures).

(13) Managing active acquisition of dense meshes: Embodiments of thewearable system 200 can perform any combination of the following actionsfor passive transmodal intent: Identifies the ROI defined by transmodalvergence and fixation; Identifies the corresponding interaction field(near/mid/far); Accelerate the processing dense meshes of interest; andimproves object interaction fidelity by preparing for direct observationand interaction.

For example: foveated meshing to improve dense mesh occlusion of objectsin the near field or non-planar surface touch interactions.

(14) Managing active acquisition of planar surfaces: Embodiments of thewearable system 200 can perform any combination of the following actionsfor passive transmodal intent: Identifies the ROI defined by transmodalvergence and fixation; Identifies the corresponding interaction field(near/mid/far); Accelerate the processing of fast planes of interest;and Improves surface interaction fidelity by preparing for directobservation and interaction.

For example: to improve surface touch interactions and actively reduceerrors in surface touch tracking.

(15) Managing active acquisition of dynamic found entities: Embodimentsof the wearable system 200 can perform any combination of the followingactions for active transmodal intent: Identifies the ROI defined bytransmodal vergence, fixation and smooth pursuit; Identifies thecorresponding interaction field (near/mid/far); Accelerate theprocessing of found entities of interest; and Improves found entityinteraction fidelity by preparing for dynamic motion or interaction.

For example: Preemptively preparing the system for interaction withfound entities can reduce the apparent latency of dynamic found entitytracking.

(16) Managing active grasping of dynamic found entities: Embodiments ofthe wearable system 200 can perform any combination of the followingactions for active transmodal intent: Identifies the ROI defined bytransmodal vergence, fixation and smooth pursuit; Identifies thecorresponding interaction field (near/mid/far); Accelerate theprocessing of found entities of interest; Enable the local processing offound entities of interest; and Improves found entity interactionfidelity by preparing for hand grasp based dynamic interaction.

For example: Preemptively preparing the system for interaction with handtracking with grasped objects can reduce the apparent latency of graspedobject tracking, or improve real-time object segmentation methods andimprove hand pose estimation when grasping objects. Frequently graspedobjects can result in better segmentation models in the cloud which canbe used for a personalized user specific optimizations for grasped handtracking.

The foregoing functionalities can be provided by various implementationsof the transmodal input fusion techniques and are not limiting. Wearablesystems (e.g., such as the wearable system 200) can perform embodimentsof one, some, or all of these techniques or may perform additional ordifferent transmodal input fusion techniques. Many variations andcombinations are possible.

Example Software Code

Appendix A includes an example of code in the C# programming languagethat can be used to perform an example implementation of the transmodalinput fusion technology described herein. In various implementations,the program in Appendix A can be performed by the local processing anddata module 260, the remote processing module 270, the central runtimeserver 1650, the processor 128, or other processor associated with thewearable system 200. The disclosure of Appendix A is intended toillustrate an example implementation of various features of thetransmodal input fusion technology and is not intended to limit thescope of the technology. Appendix A is hereby incorporated by referenceherein in its entirety so as to form a part of this specification.

Example Neurophysiological Approaches to Transmodal Input FusionTechniques

Without intending to be bound or limited by any particularneurophysiological model or sensorimotor paradigm, certain embodimentsof the transmodal input fusion systems and methods may apply or leverageteachings from such models or paradigms to, e.g., sensor convergence ordivergence, transmodal input fusion techniques, sensor input filtering,identification or characterization of the transmodal state, operationsto be performed using transmodally fused inputs, and so forth.

For example, many leading theories in the area of neurophysiologycontend that the kinematic and dynamic properties of human motorbehavior are too vast and complex to reasonably be governed by a singleinternal model or computational scheme. Instead, it has been postulatedthat the human brain employs a modular computational architecture.Examples of theoretical architectures that may be useful in describingthis human sensorimotor paradigm or aspects thereof may include MultiplePaired Forward-Inverse Models (“MPFIM”), the Mixture-of-Expertsarchitecture, the Modular Selection and Identification for Control(“MOSAIC”) model, and the like. In some implementations, certain aspectsof one or more of the systems and techniques described herein may befunctionally similar or analogous to those of such theoreticalarchitectures.

Within such an architecture, switching between multiple different“modules” (also referred to as “synergies” or “coordinative structures”)may be performed on the basis of context. Each module may physicallycorrespond to a neurophysiological mechanism, but may logicallyrepresent a complex dynamical system that may be described usingdifferential equations and configured to carry out a particular motor orsensorimotor control strategy.

In some examples, each module may contain both a forward model and aninverse model. Such an inverse model may, for instance, be seen as beingspecialized for a particular behavioral context, while a correspondingforward model may be seen as serving to determine the “responsibility”for such an inverse model in the current context. In operation, theinverse model may receive a reference input indicative of a targetsensory state, and in turn compute and provide a motor command (e.g.,one or more muscle activation signals) to the “plant” or motor unit inthe system (e.g., one or more muscles). The plant may then act out themotor command, which effectively yields a new sensory state. The inversemodel may also provide an efference copy of the motor command to theforward model, which may in turn compute a predicted sensory state. Thenew sensory state may be evaluated against the predicted sensory stateto generate an error signal, which may be leveraged as feedback by theinverse model to correct current movement or otherwise improve systemperformance. Indeed, the feedback loop formed between the forward modeland the inverse model yields an inevitable mutual influence between theinputs to and outputs of the module.

In some embodiments, one or more of the transmodal input fusion systemsand techniques described herein may seek to detect occurrences ofswitching events in which one or more modules are activated,deactivated, or a combination thereof. To do so, one or more of thesystems described herein may monitor module outputs (e.g., movements andpositions of muscles, joints, or other anatomical features capable ofbeing tracked by electronic sensing components of the system) for signsof feedback stabilization processes, which may occur immediatelyfollowing or relatively soon after the activation of a given module(e.g., by virtue of its respective step response), or signs of otheroperational bifurcation points. Such feedback stabilization processescan yield stochastic convergence between at least one pair of inputs toor outputs from the module. That is, inputs to or outputs from themodule can become increasingly influential on each other as the moduleinitially stabilizes itself upon activation. For example, changes instatistical variance, covariance, or correlation between a given pair ofmodule outputs monitored by the system may be indicative of convergenceevents (e.g., module activation, increase in module controlcontribution, etc.), divergence events (e.g., module deactivation,decrease in module control contributions, etc.), and the like.

Additional Considerations

Although certain examples of transmodal input fusion have been describedherein in the context of AR/MR/VR systems, this is for illustration andnot limitation. Embodiments of the transmodal input fusion techniquesdescribed herein can be applied to, for example, robotics, drones,user-guided perception, human-machine interaction, human-computerinteraction, brain-computer interfaces, user experience design, and soforth. For example, a robotic system or drone can have multiple inputmodes and transmodal techniques can be used to dynamically determinewhich of the multiple inputs have converged and utilize the convergedinput modes as set forth above.

Each of the processes, methods, and algorithms described herein and/ordepicted in the attached figures may be embodied in, and fully orpartially automated by, code modules executed by one or more physicalcomputing systems, hardware computer processors, application-specificcircuitry, and/or electronic hardware configured to execute specific andparticular computer instructions. For example, computing systems caninclude general purpose computers (e.g., servers) programmed withspecific computer instructions or special purpose computers, specialpurpose circuitry, and so forth. A code module may be compiled andlinked into an executable program, installed in a dynamic link library,or may be written in an interpreted programming language. In someimplementations, particular operations and methods may be performed bycircuitry that is specific to a given function.

Further, certain implementations of the functionality of the presentdisclosure are sufficiently mathematically, computationally, ortechnically complex that application-specific hardware or one or morephysical computing devices (utilizing appropriate specialized executableinstructions) may be necessary to perform the functionality, forexample, due to the volume or complexity of the calculations involved orto provide results substantially in real-time. For example, a video mayinclude many frames, with each frame having millions of pixels, andspecifically programmed computer hardware is necessary to process thevideo data to provide a desired image processing task or application ina commercially reasonable amount of time. Further, the transmodaltechniques can utilize dynamic monitoring of sensor inputs to detectconvergence and divergence events and may utilize complex hardwareprocessor or firmware based solutions in order to execute in real time.

Code modules or any type of data may be stored on any type ofnon-transitory computer-readable medium, such as physical computerstorage including hard drives, solid state memory, random access memory(RAM), read only memory (ROM), optical disc, volatile or non-volatilestorage, combinations of the same and/or the like. The methods andmodules (or data) may also be transmitted as generated data signals(e.g., as part of a carrier wave or other analog or digital propagatedsignal) on a variety of computer-readable transmission mediums,including wireless-based and wired/cable-based mediums, and may take avariety of forms (e.g., as part of a single or multiplexed analogsignal, or as multiple discrete digital packets or frames). The resultsof the disclosed processes or process steps may be stored, persistentlyor otherwise, in any type of non-transitory, tangible computer storageor may be communicated via a computer-readable transmission medium.

Any processes, blocks, states, steps, or functionalities in flowdiagrams described herein and/or depicted in the attached figures shouldbe understood as potentially representing code modules, segments, orportions of code which include one or more executable instructions forimplementing specific functions (e.g., logical or arithmetical) or stepsin the process. The various processes, blocks, states, steps, orfunctionalities can be combined, rearranged, added to, deleted from,modified, or otherwise changed from the illustrative examples providedherein. In some embodiments, additional or different computing systemsor code modules may perform some or all of the functionalities describedherein. The methods and processes described herein are also not limitedto any particular sequence, and the blocks, steps, or states relatingthereto can be performed in other sequences that are appropriate, forexample, in serial, in parallel, or in some other manner. Tasks orevents may be added to or removed from the disclosed exampleembodiments. Moreover, the separation of various system components inthe implementations described herein is for illustrative purposes andshould not be understood as requiring such separation in allimplementations. It should be understood that the described programcomponents, methods, and systems can generally be integrated together ina single computer product or packaged into multiple computer products.Many implementation variations are possible.

The processes, methods, and systems may be implemented in a network (ordistributed) computing environment. Network environments includeenterprise-wide computer networks, intranets, local area networks (LAN),wide area networks (WAN), personal area networks (PAN), cloud computingnetworks, crowd-sourced computing networks, the Internet, and the WorldWide Web. The network may be a wired or a wireless network or any othertype of communication network.

The systems and methods of the disclosure each have several innovativeaspects, no single one of which is solely responsible or required forthe desirable attributes disclosed herein. The various features andprocesses described above may be used independently of one another, ormay be combined in various ways. All possible combinations andsubcombinations are intended to fall within the scope of thisdisclosure. Various modifications to the implementations described inthis disclosure may be readily apparent to those skilled in the art, andthe generic principles defined herein may be applied to otherimplementations without departing from the spirit or scope of thisdisclosure. Thus, the claims are not intended to be limited to theimplementations shown herein, but are to be accorded the widest scopeconsistent with this disclosure, the principles and the novel featuresdisclosed herein.

Certain features that are described in this specification in the contextof separate implementations also can be implemented in combination in asingle implementation. Conversely, various features that are describedin the context of a single implementation also can be implemented inmultiple implementations separately or in any suitable subcombination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination. No single feature orgroup of features is necessary or indispensable to each and everyembodiment.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list. In addition, thearticles “a,” “an,” and “the” as used in this application and theappended claims are to be construed to mean “one or more” or “at leastone” unless specified otherwise.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y and atleast one of Z to each be present.

The term “threshold,” as used herein, refers to any possible type ofthreshold. As examples, the term “threshold” includes predefinedthresholds, dynamically determined thresholds, dynamically adjustedthresholds, and learned thresholds (e.g., thresholds learned throughuser interactions, thresholds based on user preferences, thresholdsbased on user abilities, etc.). A threshold based on user abilities maybe adjusted up or down based on individual user's ability. As anexample, a touchpad force threshold that is based on user abilities maybe adjusted down for user's with weaker than normal finger strength(which may be learned from prior interactions by the user or learnedthrough user preferences).

Similarly, while operations may be depicted in the drawings in aparticular order, it is to be recognized that such operations need notbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Further, the drawings may schematically depict one more exampleprocesses in the form of a flowchart. However, other operations that arenot depicted can be incorporated in the example methods and processesthat are schematically illustrated. For example, one or more additionaloperations can be performed before, after, simultaneously, or betweenany of the illustrated operations. Additionally, the operations may berearranged or reordered in other implementations. In certaincircumstances, multitasking and parallel processing may be advantageous.Moreover, the separation of various system components in theimplementations described above should not be understood as requiringsuch separation in all implementations, and it should be understood thatthe described program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts. Additionally, other implementations are within the scope ofthe following claims. In some cases, the actions recited in the claimscan be performed in a different order and still achieve desirableresults.

EXAMPLES

Various examples of systems that dynamically fuse multiple modes of userinput to facilitate interacting with virtual objects in athree-dimensional (3D) environment are described herein such as theexamples enumerated below:

Example 1: A system comprising: a first sensor of a wearable systemconfigured to acquire first user input data in a first mode of input, asecond sensor of the wearable system configured to acquire second userinput data in a second mode of input, the second mode of input differentfrom the first mode of input, a third sensor of the wearable systemconfigured to acquire third user input data in a third mode of input,the third mode of input different from the first mode of input and thesecond mode of input, and a hardware processor in communication with thefirst, second, and third sensors, the hardware processor programmed toreceive multiple inputs comprising the first user input data in thefirst mode of input, the second user input data in the second mode ofinput, and the third user input data in the third mode of input,identify a first interaction vector based on the first user input data,identify a second interaction vector based on the second user inputdata, identify a third interaction vector based on the third user inputdata, determine a vergence among at least two of the first interactionvector, the second interaction vector, and the third interaction vector,identify, based at least partly on the vergence, a target virtual objectfrom a set of candidate objects in a three-dimensional (3D) regionaround the wearable system, determine a user interface operation on thetarget virtual object based on at least one of the first user inputdata, the second user input data, the third user input data, and thevergence, and generate a transmodal input command that causes the userinterface operation to be performed on the target virtual object.

Example 2: The system of Example 1, wherein the first sensor comprises ahead pose sensor, the second sensor comprises an eye gaze sensor, andthe third sensor comprises a hand gesture sensor.

Example 3: The system of Examples 1 or 2, wherein the vergence is amongall three of the first interaction vector, the second interactionvector, and the third interaction vector.

Example 4: The system of any of Examples 1-3, wherein the hardwareprocessor is programmed to determine a divergence of at least one of thefirst interaction vector, the second interaction vector, or the thirdinteraction vector from the vergence.

Example 5: The system of any of Examples 1-4, wherein to determine avergence among at least two of the first interaction vector, the secondinteraction vector, and the third interaction vector, the hardwareprocessor is programmed to determine a verged data set comprising userinput data associated with sensors determined to have verged.

Example 6: The system of any of Examples 1-5, wherein the third sensorcomprises an electromyogram (EMG) sensor sensitive to hand motions.

Example 7: The system of any of Examples 1-5, wherein the third sensorcomprises an electromyogram (EMG) sensor sensitive to muscles passingthrough a user's carpal tunnel.

Example 8: A method comprising: under control of a hardware processor ofa wearable system: accessing sensor data from a plurality of greaterthan three sensors of different modalities, identifying a convergenceevent of a first sensor and a second sensor from the plurality ofgreater than three sensors of different modalities, and utilizing firstsensor data from the first sensor and second sensor data from the secondsensor to target an object in a three-dimensional (3D) environmentaround the wearable system.

Example 9: The method of Example 8, further comprising identifying asecond convergence event of a third sensor fusing with the first sensorand the second sensor, the third sensor from the plurality of greaterthan three sensors of different modalities and wherein utilizing firstsensor data from the first sensor and second sensor data from the secondsensor to target an object in a three-dimensional (3D) environmentaround the wearable system, further comprises utilizing third sensordata from third sensor.

Example 10: The method of any of Examples 8 or 9, further comprisingidentifying a divergence event wherein the first sensor diverges fromthe second sensor or the third sensor diverges from the first sensor orthe second sensor.

Example 11: The method of any of Example 10, wherein said utilizing doesnot include utilizing data from a diverged sensor.

Example 12: The method of Example 10, wherein said utilizing comprisesweighting data from a diverged sensor less than data from convergedsensors.

Example 13: The method of any of Examples 10-12, wherein the pluralityof greater than three sensors of different modalities comprises a headpose sensor, an eye gaze sensor, a hand gesture sensor, and a touchsensor.

Example 14: The method of any of Examples 8-13, wherein the first sensorcomprises an electromyogram (EMG) sensor sensitive to hand motions.

Example 15: The method of any of Examples 8-13, wherein the first sensorcomprises an electromyogram (EMG) sensor sensitive to hand motions,wherein the second sensor comprises a camera-based gesture sensor, andwherein identifying the convergence event of the first sensor and thesecond sensor comprises determining, with the EMG sensor, that a user'smuscles are flexed in a manner consistent with a nonverbal symbol anddetermining, with the camera-based hand gesture sensor, that at least aportion of a hand of the user is positioned in a manner consistent withthe nonverbal symbol.

Example 16: A method comprising: under control of a hardware processorof a wearable system: accessing sensor data from at least first andsecond sensors of different modalities, wherein the first sensorprovides sensor data having multiple potential interpretations,identifying a convergence of sensor data from the second sensor and of agiven one of the potential interpretations of the sensor data from thefirst sensor and generating an input command to the wearable systembased on the given one of the potential interpretations.

Example 17: The method of Example 16, wherein generating the inputcommand comprises generating the input command based on the given one ofthe potential interpretations while discarding the remaining potentialinterpretations.

Example 18: The method of any of Examples 16 or 17, wherein the firstsensor comprises a gesture sensor that tracks movement of a user's hand.

Example 19: The method of any of Examples 16-18, wherein the firstsensor comprises a gesture sensor that tracks movement of a user's arm.

Example 20: The method of any of Examples 16-19, wherein the potentialinterpretations of the sensor data from the first sensor include a firstraycast or conecast from a user's wrist to the user's fingertip andinclude a second raycast or conecast from the user's head to the user'sfingertip.

Example 21: The method of any of Examples 16-20, wherein the secondsensor comprises an eye tracking sensor and wherein identifying theconvergence comprises determining that a user's gaze and the firstraycast or conecast are approximately pointing to a common point inspace.

Example 22: A method comprising: under control of a hardware processorof a wearable system: accessing sensor data from a plurality sensors ofdifferent modalities, identifying convergence events of sensor data fromfirst and second sensors of the plurality of sensors, and during theconvergence events, selectively applying a filter to the sensor datafrom the first sensor.

Example 23: The method Example 22, wherein selectively applying thefilter to the sensor data from the first sensor during the convergenceevents comprises detecting an initial convergence of the sensor datafrom the first and second sensors and, based on the detected initialconvergence, applying the filter to the sensor data from the firstsensor.

Example 24: The method of any of Examples 22 or 23, wherein selectivelyapplying the filter to the sensor data from the first sensor during theconvergence events comprises detecting a convergence of the sensor datafrom the first and second sensors, based on the detected convergence,applying the filter to the sensor data from the first sensor, detectinga divergence of the sensor data of the first sensor from the sensor dataof the second sensor, and based on the detected divergence, disablingapplication of the filter to the sensor data from the first sensor.

Example 25: The method of any of Examples 22-24, wherein the filtercomprises a low-pass filter having an adaptive cutoff frequency.

Example 26: A wearable system comprising a hardware processor programmedto perform the method of any of Examples 8-25.

Example 27: The wearable system of Example 26, comprising at least firstand second sensors of different modalities.

Example 28: A wearable system comprising: a head pose sensor configuredto determine a head pose of a user of the wearable system, an eye gazesensor configured to determine an eye gaze direction of the user of thewearable system, a gesture sensor configured to determine a hand gestureof the user of the wearable system, a hardware processor incommunication with the head pose sensor, the eye gaze sensor, and thegesture sensor, the hardware processor programmed to: determine a firstvergence between the eye gaze direction and the head pose of the userrelative to an object, perform a first interaction command associatedwith the object based at least partly on inputs from the head posesensor and the eye gaze sensor, determine a second vergence of the handgesture with the eye gaze direction and the head pose of the userrelative to the object, and perform a second interaction commandassociated with the object based at least partly on inputs from the handgesture, the head pose sensor, and the eye gaze sensor.

Example 29: The wearable system of Example 28, wherein the head posesensor comprises an inertial measurement unit (IMU), the eye gaze sensorcomprise an eye-tracking camera, and the gesture sensor comprises anoutward-facing camera.

Example 30: The wearable system of any of Examples 28 or 29, wherein todetermine the first vergence, the hardware processor is programmed todetermine that an angle between the eye gaze direction and a head posedirection associated with the head pose is less than a first threshold.

Example 31: The wearable system of any of Examples 28-30, wherein todetermine the second vergence, the hardware processor is programmed todetermine that a transmodal triangle associated with the hand gesture,the eye gaze direction, and the head pose is less than a secondthreshold.

Example 32: The wearable system of any of Examples 28-31, wherein thefirst interaction command comprises targeting the object.

Example 33: The wearable system of any of Examples 28-32, wherein thesecond interaction command comprises selecting the object.

Example 34: The wearable system of any of Examples 28-33, wherein thehardware processor is further programmed to determine a divergence of atleast one of the hand gesture, the eye gaze direction, or the head posefrom the object.

Example 35: The wearable system of any of Examples 28-34, wherein thefirst interaction command comprises applying a first filter or thesecond interaction command comprises applying a second filter.

Example 36: The wearable system of Example 35, wherein the first filteris different from the second filter.

Example 37: The wearable system of any of Examples 35 or 36, wherein thefirst filter or the second filter comprises a low-pass filter having anadaptive cutoff frequency.

Example 38: The wearable system of Example 37, wherein the low-passfilter comprises a one euro filter.

Example 39: The wearable system of any of Examples 28-38, wherein todetermine the first vergence, the hardware processor is programmed todetermine that a dwell time of the eye gaze direction and the head posetoward the object exceeds a first dwell time threshold.

Example 40: The wearable system of any of Examples 28-39, wherein todetermine the second vergence, the hardware processor is programmed todetermine that a dwell time of the eye gaze direction, the head pose,and the hand gesture relative to the object exceeds a second dwell timethreshold.

Example 41: The wearable system of any of Examples 28-40, wherein thefirst interaction command or the second interaction command comprisesproviding a stabilized targeting vector associated with the object.

Example 42: The wearable system of Example 41, wherein the hardwareprocessor provides the stabilized targeting vector to an application.

Example 43: The wearable system of any of Examples 28-42, wherein thegesture sensor comprises a handheld user input device.

Example 44: The wearable system of Example 43, wherein the hardwareprocessor is programmed to determine a third vergence between input fromthe user input device and at least one of the eye gaze direction, thehead pose, or the hand gesture.

Example 45: The wearable system of any of Examples 28-44, furthercomprising a voice sensor, and wherein the hardware processor isprogrammed to determine a fourth vergence between input from the voicesensor and at least one of the eye gaze direction, the head pose, or thehand gesture.

Example 46: A method comprising: under control of a hardware processorof a wearable system: identifying a current transmodal state, thecurrent transmodal state comprising a transmodal vergence associatedwith an object, identifying a region of intent (ROI) associated with thetransmodal vergence, identifying a corresponding interaction field basedat least partly on the ROI, selecting an input fusion method based atleast partly on the transmodal state, selecting settings for a primarytargeting vector, applying conditioning to the primary targeting vectorto provide us stabilized pose vector, and communicating the stabilizedpose vector to application.

Example 47: The method of Example 46, wherein the correspondinginteraction field comprises one or more of: a near field, a midfield, ora far field.

Example 48: The method of any of Examples 46 or 47, wherein applyingconditioning comprises reducing registration error, jitter, or drift ofthe primary targeting vector.

Example 49: The method of any of Examples 46-48, further comprisingtargeting the object.

Example 50: The method of any of Examples 46-49, wherein identifying thecurrent transmodal state comprises determining a fixation or a dwell.

Example 51: The method of Example 50, further comprising: determining ifthe fixation or the dwell exceeds a user focus threshold and, inresponse to a determination that the fixation or the dwell exceeds theuser focus threshold, activating microgesture manipulations.

Example 52: The method of any of Examples 46-51, wherein identifying thecorresponding interaction field comprises identifying a field transitionevent, the field transition event comprising a transition between afirst interaction field and a second interaction field.

Example 53: The method of any of Examples 46-52, wherein identifying thecurrent transmodal state comprises analyzing convergence among aplurality of input targeting vectors.

Example 54: The method of Example 53, wherein analyzing convergencecomprises determining an angular distance between pairs of the pluralityof input target vectors.

Example 55: The method of Example 54, further comprising determining arelative variance between each pair of the plurality of input targetvectors.

Example 56: The method of Example 55, further comprising: determiningthat the angular distance of a pair of input target vectors is below afirst threshold and the relative variance of the pair of input targetvectors is below a second threshold and, in response to the determining,identifying the current transmodal state as a bimodal state associatedwith the pair of input target vectors.

Example 57: The method of any of Examples 53-56, wherein analyzingconvergence comprises determining that a triplet of input target vectorsis associated with a transmodal triangle having an area and three sides.

Example 58: The method of Example 57, comprising: determining that: thearea of the transmodal triangle is below a third threshold, a variancein the area is below a fourth threshold or variances in lengths of thesides of the transmodal triangle are below a fifth threshold and, inresponse to the determining, identifying the current transmodal state asa trimodal state associated with the triplet of input target vectors.

Example 59: The method of any of Examples 46-58, wherein the currenttransmodal state comprises a bimodal state, a trimodal state, or aquadmodal state.

Example 60: A method comprising: under control of a hardware processorof a wearable system: identifying a transmodal fixation point, definingan extended region of interest (ROI) based on a transmodal fixation timeor a predicted dwell time near the transmodal fixation point,determining that the ROI intersects with a rendering element,determining a rendering augmentation that is compatible with the ROI,the transmodal fixation time, or the predicted dwell time, andactivating the rendering augmentation.

Example 61: The method of Example 60, wherein the rendering augmentationcomprises one or more of: a reflection map, a surface shimmer affect,subsurface scattering, gaseous lensing or refraction, particle counts,or an advanced high dynamic range (HDR) rendering or lighting method.

Example 62: The method of any of Examples 60 or 61, wherein therendering augmentation is activated only during the predicted dwell timeor the transmodal fixation time.

Example 63: The method of any of Examples 60-62, further comprising:detecting a divergence of a previously converged input modality anddeactivating the rendering augmentation.

Example 64: A wearable system comprising a hardware processor programmedto perform the method of any of Examples 46-63.

Example 65: The wearable system of Example 64, comprising at least firstand second sensors of different modalities.

Example 66: The wearable system of Example 65, wherein the at leastfirst and second sensors of different modalities comprise: a head posesensor, an eye gaze sensor, a gesture sensor, a voice sensor, or ahandheld user input device.

Example 67: A method comprising: under control of a hardware processorof a wearable system: receiving sensor data from a plurality sensors ofdifferent modalities; determining that data from a particular subset ofthe plurality of sensors of different modalities indicates that a useris initiating execution of a particular motor or sensorimotor controlstrategy from among a plurality of predetermined motor and sensorimotorcontrol strategies; selecting, from among a plurality of differentsensor data processing schemes that each correspond to a different oneof the plurality of predetermined motor and sensorimotor controlstrategies, a particular sensor data processing scheme that correspondsto the particular motor or sensorimotor control strategy; and processingdata received from the particular subset of the plurality of sensors ofdifferent modalities according the particular sensor data processingscheme.

Example 68: The method of Example 67, wherein determining that data fromthe particular subset of the plurality of sensors of differentmodalities indicates that the user is initiating execution of aparticular motor or sensorimotor control strategy from among theplurality of predetermined motor and sensorimotor control strategiescomprises: determining that data from the particular subset of theplurality of sensors of different modalities is stochasticallyconverging.

Example 69: The method of any of Examples 67-68 further comprising:while processing data received from the particular subset of theplurality of sensors of different modalities according the particularsensor data processing scheme: determining that data from the particularsubset of the plurality of sensors of different modalities indicatesthat the user is concluding execution of the particular motor orsensorimotor control strategy; in response to determining that data fromthe particular subset of the plurality of sensors of differentmodalities indicates that the user is concluding execution of theparticular motor or sensorimotor control strategy: refraining fromprocessing data received from the particular subset of the plurality ofsensors of different modalities according the particular sensor dataprocessing scheme.

Example 70: The method of Example 69, wherein determining that data fromthe particular subset of the plurality of sensors of differentmodalities indicates that the user is concluding execution of theparticular motor or sensorimotor control strategy comprises: determiningthat data from the particular subset of the plurality of sensors ofdifferent modalities is stochastically diverging.

Example 71: The method of any of Examples 67-70, wherein processing datareceived from the particular subset of the plurality of sensors ofdifferent modalities according the particular sensor data processingscheme comprises: filtering data from one or more of the particularsubset of the plurality of sensors of different modalities in aparticular manner.

Example 72: The method of any of Examples 67-71, wherein processing datareceived from the particular subset of the plurality of sensors ofdifferent modalities according the particular sensor data processingscheme comprises: fusing data from the particular subset of theplurality of sensors of different modalities in a particular manner.

Example 73: The method of Examples 67-72, wherein determining that datafrom the particular subset of the plurality of sensors of differentmodalities indicates that the user is initiating execution of aparticular motor or sensorimotor control strategy from among theplurality of predetermined motor and sensorimotor control strategiescomprises: determining that one or more statistical parametersdescribing one or more relationships between data from the particularsubset of the plurality of sensors of different modalities satisfies oneor more threshold values.

Example 74: The method of Example 73, wherein one or more statisticalparameters comprise one or more of a variance, covariance, orcorrelation.

Example 75: A method comprising: under control of a hardware processorof a wearable system: receiving sensor data from a plurality sensors ofdifferent modalities; determining that data from a particular subset ofthe plurality of sensors of different modalities is stochasticallychanging in a particular manner; in response to determining that datafrom the particular subset of the plurality of sensors of differentmodalities is stochastically changing in the particular manner,switching between: processing data received from the particular subsetof the plurality of sensors of different modalities according a firstsensor data processing scheme; and processing data received from theparticular subset of the plurality of sensors of different modalitiesaccording a second sensor data processing scheme different from thefirst sensor data processing scheme.

Example 76: The method of Example 75, wherein determining that data fromthe particular subset of the plurality of sensors of differentmodalities is stochastically changing in the particular manner comprisesdetermining that data from the particular subset of the plurality ofsensors of different modalities is stochastically converging.

Example 77: The method of any of Examples 76 or 77, wherein determiningthat data from the particular subset of the plurality of sensors ofdifferent modalities is stochastically changing in the particular mannercomprises determining that data from the particular subset of theplurality of sensors of different modalities is stochasticallydiverging.

Example 78: A wearable system comprising a hardware processor programmedto perform the method of any of Examples 67-77.

Any of the above Examples can be combined with any of the other Examplesor any of the other features described in this application. The Examplesare not intended to exclude additional elements described herein. Allpossible combinations and subcombinations of the Examples, with orwithout additional features described herein, are contemplated andconsidered part of this disclosure.

APPENDIX A Copyright Statement

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

The following computer code and description are intended to illustratevarious embodiments of the transmodal input fusion technology but arenot intended to limit the scope of the transmodal input fusiontechnology. In various implementations, this computer code can beperformed by the local processing and data module 260, the remoteprocessing module 270, the central runtime server 1650, or otherprocessor associated with the wearable system 200.

C# Script

The script disclosed herein illustrates how context can be derived fromusing 2D context fusion calculations and then used to qualify dynamicfiltering of primary targeting vector (hand). Filtering to primarytarget vector pose can be ramped up using linear and sphericalinterpolation methods.

Calculation list:

-   -   Head-Hand midpoint current position    -   Hand-Eye midpoint current position    -   Head-Eye midpoint current position    -   Head-Hand midpoint current distance    -   Hand-Eye midpoint current distance    -   Head-Eye midpoint current distance    -   Head-eye-Hand triangle current center position    -   Head-eye-Hand triangle current area    -   /* used for variance calculation*/Head    -   (H) interaction point (gaze) Mean position: rolling window 10        frames.    -   Eye (E) interaction point (vergence) Mean position: rolling        window 10 frames.    -   Hand (Ha) Interaction Point (index finger tip) Mean position:        rolling window 10 frames.    -   Head-Hand midpoint Mean position: rolling window 10 frames.    -   Hand-Eye midpoint Mean position: rolling window 10 frames.    -   Head-Eye midpoint Mean position: rolling window 10 frames.    -   Head-Eye-Hand centerpoint Mean position: rolling window 10        frames.    -   Head-Eye-Hand mean triangle area: rolling window 10 frames.    -   Head interaction point (gaze) position variance: rolling window        10 frames.    -   Eye interaction point (vergence) position variance: rolling        window 10 frames.    -   Hand Interaction Point (index finger tip) position variance:        rolling window 10 frames.    -   Head-Hand midpoint position variance: rolling window 10 frames.    -   Hand-Eye midpoint position variance: rolling window 10 frames.    -   Head-Eye midpoint position variance: rolling window 10 frames.    -   Head-Eye-Hand centerpoint position variance: rolling window 10        frames.    -   Head-Eye-Hand triangle area variance: rolling window 10 frames.    -   Head interaction point (gaze) current velocity (xy tangential        component)    -   Eye interaction point (vergence) current velocity (xy tangential        component)    -   Hand Interaction Point (index finger tip) current velocity (xy        tangential component)    -   Head-Hand midpoint current velocity (xy tangential component)    -   Hand-Eye midpoint current velocity (xy tangential component)    -   Head-Eye midpoint current velocity (xy tangential component)    -   Head-Eye-Hand centerpoint current velocity (xy tangential        component)    -   Head interaction point (gaze) mean velocity: rolling window 20        frames (xy tangential component)    -   Eye interaction point (vergence) mean velocity: rolling window        20 frames (xy tangential component)    -   Hand Interaction Point (index finger tip) mean velocity: rolling        window 20 frames (xy tangential component)    -   Head-Hand midpoint mean velocity: rolling window 10 frames (xy        tangential component)    -   Hand-Eye midpoint mean velocity: rolling window 10 frames (xy        tangential component)    -   Head-Eye midpoint mean velocity: rolling window 10 frames (xy        tangential component)    -   Head-Eye-Hand centerpoint mean velocity: rolling window 10        frames (xy tangential component)    -   Head interaction point (gaze) Mean acceleration: rolling window        20 frames (xy tangential component)    -   Eye interaction point (vergence) Mean acceleration: rolling        window 20 frames (xy tangential component)    -   Hand Interaction Point (index finger tip) Mean acceleration:        rolling window 20 frames (xy tangential component)    -   Head interaction point (gaze) current acceleration (xy        tangential component)    -   Eye interaction point (vergence) current acceleration (xy        tangential component)    -   Hand Interaction Point (index finger tip) current acceleration        (xy tangential component)    -   Head-Hand midpoint current acceleration (xy tangential        component)    -   Hand-Eye midpoint current acceleration (xy tangential component)    -   Head-Eye midpoint current acceleration (xy tangential component)    -   Head-Eye-Hand centerpoint current acceleration (xy tangential        component)    -   Head-Eye-Hand mean triangle center current acceleration (xy        tangential component)

Dynamic Filtering First-Turn-on Pseudocode:/**************************************************************//*classify basic vergence union feedback*//**************************************************************/ /*limitthe max length of “fused” bimodal input vectors in feedback display*/ /*defines max allowable vergence union distance*/ if( Ha-E dist < 5 deg){draw bi-modal line } If (H-E dist < 10 deg){ draw bi-modal line } If(H-Ha dist < 10 deg){ draw bi-modal line } If (Ha-E dist < 5 deg && H-Edist < 10 deg && H-Ha dist < 10 deg){ draw tri-modal triangle area fill} /**************************************************************//*classify dynamic vergence state based on motion*//**************************************************************//*tri-modally fused inputs*/ /*trimodal fixation*/ If (triangle area <<Area0 && H-E-Ha-area-variance< var0 && average triangle velocity < V0 &&mean triangle acceleration < A0){ trimodal_vergence = true;timodal_fixation = true; } /*trimodal ballistic pursuit*/ else if(triangle area < Area1 && H-E-Ha-area-variance< var0 && average trianglevelocity < V1 && average triangle acceleration<A2) { trimodal_vergence =true; trimodal_balistic_pursuit = true; } /*trimodal smooth pursuit*/else if (triangle area < Area1 && H-E-Ha-area-variance< var0 && averagetriangle velocity < V2 && average triangle acceleration<A4) {trimodal_vergence = true; trimodal_smooth_pursuit = true; } Else{trimodal_vergence = false; trimodal_fixation = false;trimodal_smooth_pursuit = false; trimodal_ballisitc_pursuit = false; }/**************************************************************//*bi-modally fused inputs*/ /* hand-eye bimodal (Ha-E) fixation*/ elseif (Ha-E-dist< dist1 && Ha-E-dist-variance< var1 && Ha-E averagevelocity < V2 && Ha-E average acceleration < A3){ bimodal_vergence =true; bimodal_fixation = true; } /* head-eye bimodal (H-E) fixation*/else if (H-E-dist< dist2 && H-E-dist-variance< var2 && H-E averagevelocity < V2 && H-E average acceleration < A3){ bimodal_vergence =true; bimodal_fixation = true; } /*hand-eye bimodal (Ha-E) smoothpursuit*/ else if (Ha-E-dist< dist1 && H-E-dist-variance< var2 && Ha-Eaverage velocity < V3 && Ha-E average acceleration < A4){bimodal_vergence = true; bimodal_smooth_pursuit = true; } /*head-eyebimodal (H-E) smooth pursuit*/ else if (H-E-dist< dist1 &&H-E-dist-variance< var2 && H-E average velocity < V3 && H-E averageacceleration < A4){ bimodal_vergence = true; bimodal_smooth_pursuit =true; } /*hand-eye bimodal (Ha-E) ballistic pursuit*/ else if(Ha-E-dist< dist1 && H-E-dist-variance< var2 && Ha-E average velocity <V4 && Ha-E average acceleration < A5){ bimodal_vergence = true;biimodal_ballistic _pursuit = true; } /*head-eye bimodal (H-E) ballisticpursuit*/ else if (H-E-dist< dist1 && H-E-dist-variance< var2 && H-Eaverage velocity < V4 && H-E average acceleration < A5){bimodal_vergence = true; bimodal_ballistic_pursuit = true; } Else{bimodal_vergence = false; bimodal_fixation = false;bimodal_smooth_pursuit = false; bimodal_ballistic_pursuit = false; }/**************************************************************//*manage dynamic filtering based on input fusion state*//**************************************************************//*tri-modally fused inputs*/ If (trimodal_vergence){ If(trimodal_fixation) activate euro filter (strong) on raw hand position(xyz) and orientation Else if (trimodal_smooth_pursuit) activate eurofilter (strong) on raw hand position (xyz) and orientation Else if(trimodal_balisitc_pursuit) activate euro filter (strong) on raw handposition (xyz) and orientation } If (bimodal_vergence){ If(bimodal_fixation) { If (H-E) activate euro filter (medium) on raw handposition (xyz) and orientation Else if (Ha-E) activate euro filter(medium) on raw hand position (xyz) and orientation //Else if (H-Ha)activate euro filter (medium) on raw hand position (xyz) and orientation} Else if (bimodal_smooth_pursuit) { If (H-E) activate euro filter(weak) on raw eye position (xyz) and orientation Else if (Ha-E) activateeuro filter (weak) on raw hand position (xyz) and orientation //Else if(H-Ha) activate euro filter (weak) on raw hand position (xyz) andorientation } Else if (bimodal_balisitc_pursuit){ If (H-E) // do nothingElse if (Ha-E) // do nothing //Else if (H-Ha) // do nothing } }/**************************************************************//*integration management*//**************************************************************/ If(filter active && active-time< threshold){ ease in } /**/

In some examples, when fusion conditions are broken the euro filter mayease out quickly and may, in some cases, snap. In other implementations,as filtered interaction points (in this case the hand input targetingvector) degrades from trimodal fusion to bimodal fusion, one filter iseased out while another is being eased in. In this respect multiplefilters will be operational simultaneously in order to create a smoothlytransitioning position function and avoid step functions (ordiscontinuities) in velocity and acceleration of the primary targetingvector (managed cursor).

Multimodal Dwell First-Turn-on Pseudocode:/**************************************************************//*classify basic vergence union*//**************************************************************/ /*limitthe max length of “fused” bimodal input vectors in feedback display*/ /*defines max allowable vergence union distance*/ if( Ha-E dist < 5 deg){draw bi-modal line } If (H-E dist < 10 deg){ draw bi-modal line } If(H-Ha dist < 10 deg){ draw bi-modal line } If (Ha-E dist < 5 deg && H-Edist < 10 deg && H-Ha dist < 10 deg){ draw tri-modal triangle area fill} //**************************************************************//*identify basic motion state of bimodal or trimodal fusion point*//**************************************************************//**************************************************************//*classify dynamic vergence state based on motion*//**************************************************************//*tri-modally fused inputs*/ /*trimodal fixation*/ If (triangle area <<Area0 && H-E-Ha-area-variance< var0 && average triangle velocity < VO &&mean triangle acceleration < A0){ trimodal_vergence = true;timodal_fixation = true; trimodal_fixation_count =+1; } else{trimodal_vergence = false; trimodal_fixation = false;trimodal_fixation_count = 0; trimodal_smooth_pursuit = false;trimodal_ballisitc_pursuit = false; }/**************************************************************//*bi-modally fused inputs*/ /* hand-eye bimodal (Ha-E) fixation*/ elseif (Ha-E-dist< dist1 && Ha-E-dist-variance< var1 && Ha-E averagevelocity < V2 && Ha-E average acceleration < A3){ bimodal_vergence =true; bimodal_fixation = true; bimodal_fixation_count =+1; } /* head-eyebimodal (H-E) fixation*/ else if (H-E-dist< dist2 && H-E-dist-variance<var2 && H-E average velocity < V2 && H-E average acceleration < A3){bimodal_vergence = true; trimodal_fixation = true; } If(trimodal_fixation ∥ bimodal_fixation){ transmdoal_fixation = true; }else{ transmodal_fixation = false; bimodal_vergence = false;bimodal_fixation = false; bimodal_fixation_count = 0;bimodal_smooth_pursuit = false; bimodal_ballistic_pursuit = false; }/**************************************************************//*manage extended fixation and dwell states*//**************************************************************/ If(trimodal_vergence && trimodal_fixation && trimodal_fixation_count >10){trmodal_dwell = true; trimodal_fixation_count = 0; } If(bimodal_vergence && bimodal_fixation && bimodal_fixation_count >10){bimodal_dwell = true; bimodal_fixation_count = 0; } If (trimodal_dwell ∥bimodal_dwell){ transmodal_dwell = true; } else{ transmodal_dwell =false Bimodal_dwell = false; Trimodal_dwell = false; }

What is claimed is:
 1. A system comprising: a first sensor of a wearablesystem configured to acquire first user input data in a first mode ofinput; a second sensor of the wearable system configured to acquiresecond user input data in a second mode of input, the second mode ofinput different from the first mode of input; a third sensor of thewearable system configured to acquire third user input data in a thirdmode of input, the third mode of input different from the first mode ofinput and the second mode of input; and a hardware processor incommunication with the first, second, and third sensors, the hardwareprocessor programmed to: receive multiple inputs comprising the firstuser input data in the first mode of input, the second user input datain the second mode of input, and the third user input data in the thirdmode of input; identify a first interaction vector based on the firstuser input data; identify a second interaction vector based on thesecond user input data; identify a third interaction vector based on thethird user input data; determine a vergence among at least two of thefirst interaction vector, the second interaction vector, and the thirdinteraction vector; identify, based at least partly on the vergence, atarget virtual object from a set of candidate objects in athree-dimensional (3D) region around the wearable system; determine auser interface operation on the target virtual object based on at leastone of the first user input data, the second user input data, the thirduser input data, and the vergence; and generate a transmodal inputcommand that causes the user interface operation to be performed on thetarget virtual object.
 2. The system of claim 1, wherein the firstsensor comprises a head pose sensor, the second sensor comprises an eyegaze sensor, and the third sensor comprises a hand gesture sensor. 3.The system of claim 1, wherein the vergence is among all three of thefirst interaction vector, the second interaction vector, and the thirdinteraction vector.
 4. The system of claim 1, wherein the hardwareprocessor is programmed to determine a divergence of at least one of thefirst interaction vector, the second interaction vector, or the thirdinteraction vector from the vergence.
 5. The system of claim 1, whereinto determine a vergence among at least two of the first interactionvector, the second interaction vector, and the third interaction vector,the hardware processor is programmed to determine a verged data setcomprising user input data associated with sensors determined to haveverged.
 6. A method comprising: under control of a hardware processor ofa wearable system: accessing sensor data from a plurality of greaterthan three sensors of different modalities; identifying a convergenceevent of a first sensor and a second sensor from the plurality ofgreater than three sensors of different modalities; and utilizing firstsensor data from the first sensor and second sensor data from the secondsensor to target an object in a three-dimensional (3D) environmentaround the wearable system.
 7. The method of claim 6, further comprisingidentifying a second convergence event of a third sensor fusing with thefirst sensor and the second sensor, the third sensor from the pluralityof greater than three sensors of different modalities and whereinutilizing first sensor data from the first sensor and second sensor datafrom the second sensor to target an object in a three-dimensional (3D)environment around the wearable system, further comprises utilizingthird sensor data from third sensor.
 8. The method of claim 6, furthercomprising identifying a divergence event wherein the first sensordiverges from the second sensor or the third sensor diverges from thefirst sensor or the second sensor.
 9. The method of claim 7, whereinsaid utilizing does not include utilizing data from a diverged sensor.10. The method of claim 7, wherein said utilizing comprises weightingdata from a diverged sensor less than data from converged sensors. 11.The method of claim 7, wherein the plurality of greater than threesensors of different modalities comprises a head pose sensor, an eyegaze sensor, a hand gesture sensor, and a touch sensor.
 12. A methodcomprising: under control of a hardware processor of a wearable system:accessing sensor data from at least first and second sensors ofdifferent modalities, wherein the first sensor provides sensor datahaving multiple potential interpretations; identifying a convergence ofsensor data from the second sensor and of a given one of the potentialinterpretations of the sensor data from the first sensor; and generatingan input command to the wearable system based on the given one of thepotential interpretations.
 13. The method of claim 12, whereingenerating the input command comprises generating the input commandbased on the given one of the potential interpretations while discardingthe remaining potential interpretations.
 14. The method of claim 12,wherein the first sensor comprises a gesture sensor that tracks movementof a user's hand.
 15. The method of claim 12, wherein the first sensorcomprises a gesture sensor that tracks movement of a user's arm.
 16. Themethod of claim 12, wherein the potential interpretations of the sensordata from the first sensor include a first raycast or conecast from auser's wrist to the user's fingertip and include a second raycast orconecast from the user's head to the user's fingertip.
 17. The method ofclaim 12, wherein the second sensor comprises an eye tracking sensor andwherein identifying the convergence comprises determining that a user'sgaze and the first raycast or conecast are approximately pointing to acommon point in space.